Hey friend. It's Monday, November 10, 2025. The battle for AI supremacy is being fought on two fronts: today's silicon and tomorrow's physics.
China is walling off its data centers, escalating the global tech decoupling.
Google's quantum breakthrough signals the dawn of a new computational era.
Let's get into it. Don't keep us a secret: Forward this README to your best friend
Must Know
The Lede: China has mandated that all state-funded data centers must exclusively use domestic AI accelerators, effectively banning foreign chips from government infrastructure projects.
The Details: The new guidelines force a rapid pivot to homegrown hardware, impacting companies like Nvidia and AMD who previously supplied this market. This move is designed to bolster China's domestic semiconductor industry and reduce its reliance on Western technology.
My Take: This is China's declaration of technological independence. The policy is not merely defensive; it's an offensive strategy to create a massive, state-funded captive market for its own chipmakers. This guarantees demand for companies like Huawei and SMIC, forcing their development to accelerate even if their initial products are inferior. The West loses a critical market, and the world is now firmly on a path toward two distinct, non-interoperable AI hardware stacks. The bifurcation is no longer theoretical.
The Lede: Google's Quantum AI team has demonstrated the first verifiable quantum algorithm on hardware, achieving a 13,000x speedup over the best classical supercomputers for a molecular modeling problem.
The Details: Unlike previous quantum supremacy claims, this achievement is significant because the result was verifiable, proving the quantum computer arrived at a correct, useful answer. The milestone is a critical step toward practical quantum computing for complex scientific problems like drug discovery and materials science.
My Take: Quantum computing just moved from theoretical physics to strategic advantage. The 13,000x speedup is the headline, but the real story is verifiability. For the first time, a quantum algorithm has solved a practical problem that can be checked, proving it's not just computational noise. This unlocks real investment and R&D for intractable problems where classical AI hits a hard ceiling. Google is planting its flag in the post-silicon era.
Quote of the Day
It's too dangerous to let AIs communicate in their own languages.
🤖 The Agentic Layer
My take: While nations fight over hardware, the software layer is quietly becoming autonomous, turning raw models into specialized workers.
LlamaParse is embedding high-accuracy OCR directly into agentic workflows. This is the key to automating back-office finance, turning agents into practical financial analysts. [Link]
Google's new File Search Tool for the Gemini API is a hosted RAG solution that commoditizes a core piece of the agent stack. This lowers the barrier for building powerful, data-aware agents. [Link]
The claim that Kimi K2 Thinking performs on par with GPT-5 for customer support agents shows the open-source community is now competing at the application layer, not just on base model performance. [Link]
LangChain's production-ready travel agent guide is less about travel and more about providing a concrete template for multi-tool agent architectures. This accelerates the move from agent experiments to products. [Link]
Anthropic rolling out a true memory feature for Claude is a critical step. Persistent memory moves agents from single-session tools to continuous, stateful assistants. [Link]
⚙️ The Shifting Supply Chain
My take: The geopolitical moves in hardware are creating ripple effects, forcing a strategic re-evaluation of everything from chip demand to open-source dependencies.
The increasing US adoption of Chinese open-source models creates a profound strategic vulnerability. Superior economics are forcing a dependency that runs directly counter to national security goals. [Link]
TSMC's sales growth slowdown is the first concrete data point suggesting the AI hardware boom may have peaked. This signals a potential normalization of the supply chain after a period of explosive growth. [Link]
TSMC's new NanoSheet transistor technology is the answer to slowing performance gains. This innovation is critical for maintaining the pace of AI model scaling and performance improvement. [Link]
Morgan Stanley's projection of $133B in robotics revenue for Apple by 2040 isn't about robots. It's a bet that Apple will become a dominant player in edge compute hardware for autonomous systems. [Link]
Groq's partnership with Kazakhstan shows that access to high-performance, non-Nvidia compute is becoming a strategic asset for nations. This is economic diversification via AI infrastructure. [Link]
🔬 Research Corner
Fresh off Arxiv
Real-Time Reasoning Agents: A new framework called AgileThinker proposes a hybrid approach for agents, blending reactive and planning paradigms. This is a crucial step toward agents that can operate effectively in dynamic, time-sensitive environments. [Link]
Jailbreaking in the Haystack: The NINJA attack shows that simply appending benign, model-generated text to a harmful prompt can bypass safety filters. This reveals a fundamental vulnerability in how LLMs handle long contexts. [Link]
SigmaDock for Molecular Docking: A new SE(3) diffusion model, SigmaDock, has surpassed classical physics-based methods in molecular docking for the first time. This is a major milestone for applying deep learning to drug discovery. [Link]
Open Agent Specification: A new declarative language, Agent Spec, aims to create a unified standard for defining AI agents and workflows. This is a critical piece of infrastructure for making agents reusable and interoperable across different platforms. [Link]
PuzzleMoE for Model Compression: This paper introduces a training-free method to compress Mixture-of-Experts models by up to 50% while maintaining accuracy. This directly addresses the massive memory overhead of state-of-the-art MoE architectures. [Link]
Isaac Lab Simulation Framework: The successor to Isaac Gym, this Nvidia framework combines GPU-parallel physics with photorealistic rendering. It's designed to unlock large-scale, multi-modal learning for robotics. [Link]
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Cheers, Teng Yan. See you tomorrow.