November 13, 2025 · 24-Hour AI Briefing: Baidu Unveils Wenxin 5.0, Anthropic’s $50B Compute Bet, NVIDIA Smashes Training Records, and Excel Enters the AI Agent Era
Summary:
AI innovation is accelerating simultaneously across models, compute, chips, and productivity software. Baidu launches its full-modal Wenxin 5.0 together with new Kunlun Chips, Anthropic joins the “tens-of-billions compute race,” NVIDIA sets another historic training benchmark, and Microsoft upgrades Excel into an AI-native platform.
The competition is shifting from features to full-stack strategy.
Baidu Launches Wenxin 5.0 and Kunlun Chips M100/M300, Building a Closed-Loop Model + Chip Ecosystem
Baidu officially released Wenxin 5.0, positioned as a unified native full-modal model with strong capabilities in multimodal understanding, creative generation, agent planning, and instruction following. Baidu also introduced the Kunlun Chip M100 and M300, designed respectively for large-scale inference and ultra-large-scale multimodal training, coming to market in early 2026 and early 2027.
Commentary:
Wenxin 5.0’s “native unified full-modal architecture” enhances understanding, generation, and agent-level reasoning, positioning it head-to-head with global leaders like OpenAI’s GPT-4o and Google’s Gemini.
Its deployment signals potential redefinitions across Baidu’s core businesses—search, ads, maps, autonomous driving, and more.
Kunlun M100 focuses on enterprise-scale inference to accelerate real-world AI applications, while M300 targets next-generation training competition over the next 3–5 years.
The key challenge ahead is mass production. If Baidu can deliver on performance and availability, it will strengthen its strategic position in the end-to-end AI stack.
Anthropic to Invest $50 Billion in AI Infrastructure, Partnering with Fluidstack for Independent Compute
Anthropic announced plans to invest $50 billion in building large-scale AI infrastructure in the United States, partnering with Fluidstack to develop a flexible, high-performance computing pool supporting future Claude models.
Commentary:
The partnership indicates Anthropic is no longer fully reliant on the major cloud providers (Microsoft, Google, Amazon) and is moving toward building its own compute base.
With Microsoft, OpenAI, Google, and Meta all pushing trillion-scale compute investments, Anthropic’s entry confirms that tens of billions in infrastructure is now the minimum ticket to compete at the frontier.
Compute has become the defining bottleneck for model iteration. Anthropic’s move is both strategic necessity and a reflection of escalating competitive pressure.
NVIDIA’s GB300 NVL72 Breaks Training Record: 405B Parameters Trained in 10 Minutes
NVIDIA announced that its GB300 NVL72 system trained a 405-billion-parameter model in just 10 minutes during the latest MLPerf benchmark, winning seven categories and setting a new global record.
Commentary:
Compressing training cycles to the minute scale dramatically accelerates research, deployment, and commercial responsiveness.
NVIDIA is signaling once again that its hardware and ecosystem lead remains unshaken in the short term.
However, the NVL72 system is expected to cost over $10 million per unit, with power consumption potentially exceeding 100 kW—suitable only for top-tier data centers.
This level of performance further deepens the industry’s structural reliance on NVIDIA’s compute stack.
Microsoft to Add Copilot-Powered “Agent Mode” to Excel Web in December 2025
Microsoft announced that the web version of Excel will introduce Copilot-driven Agent Mode in December 2025. The feature can autonomously create worksheets, perform data analysis, and generate charts based on user instructions, automating complex workflows.
Commentary:
Excel is officially entering the AI Agent era, transforming Office from “AI-assisted” to a truly AI-native productivity platform.
AI becomes the primary interaction layer, enabling tasks that previously required manual formulas, modeling, or visualization to be handled autonomously.
The challenge lies in trust and responsibility: if an autonomous agent produces incorrect formulas or misleading charts, who bears the cost?
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