Industry News

Tracks company news, strategic changes, funding activities, and personnel adjustments across the blockchain and crypto industries, delivering a full-spectrum industry overview for our users.

Tencent, Alibaba, ByteDance in a Battle for the Skill Store

Skill is becoming a key concept in the AI field, essentially serving as a structured "instruction manual" for AI Agents that specifies tool calls, decision logic, and output standards. This allows Agents to execute predefined tasks. As the number of Skills grows, distribution platforms have emerged. Major tech companies are swiftly entering this space. In March, Tencent, Alibaba, and ByteDance launched Skill stores within their respective Agent platforms. Subsequently, players like Zhipu AI, Meituan, and Xiaohongshu joined the fray. This competition for the "Skill store" is fundamentally a battle for the AI-era user entry point; whoever controls distribution controls the users. While ByteDance's Coze has experimented with paid Skills, most platforms offer them for free. The real value lies not in the stores themselves but in using them to attract and retain users within an ecosystem, driving revenue from services like cloud computing, model calls, or advertising. The landscape features three main player types: 1) **Internet giants** (e.g., Alibaba, ByteDance, Tencent, Meituan), leveraging Skills to drive traffic and monetize through their broader ecosystems (cloud services, transactions, ads). 2) **Large model companies** (e.g., Zhipu AI, Moonshot AI), using Skill stores to increase user engagement and monetize model API calls. 3) **Content platforms** (e.g., Xiaohongshu), treating Skills as a new content format to generate traffic and ad revenue. However, transforming Skill stores into a sustainable business faces significant hurdles. Key challenges include: the **difficulty in pricing Skills** due to inconsistent outputs across different models and contexts; **lack of cost transparency** (varying token consumption); **security risks** like Skill poisoning; and the **absence of standardized protocols** for development and evaluation. Unlike standardized mobile apps, Skills are often personalized workflows resistant to uniformity, which hinders the establishment of a reliable review and monetization system akin to the App Store. While there is genuine user demand for paid Skills—particularly in enterprise (e.g., contract review) and certain personal productivity scenarios—current platforms offer developers limited and unpredictable distribution. The future of Skill stores depends on overcoming these standardization, evaluation, and safety challenges to make acquiring a Skill as straightforward as downloading an app. For now, the stores function more as display shelves than robust marketplaces.

marsbit3 ч. назад

Tencent, Alibaba, ByteDance in a Battle for the Skill Store

marsbit3 ч. назад

After Marvell's 32% Surge, the Chinese Chip Family Behind It Emerges

The stock price of Marvell Technology surged 32.5% on June 2nd, driven by NVIDIA CEO Jensen Huang highlighting its custom ASICs and optical interconnects as core to AI data center architecture. This event brought attention to the Chinese semiconductor family behind Marvell: the Dai siblings. The story centers on three siblings, all UC Berkeley graduates, whose three-decade entrepreneurial journey aligns with major semiconductor industry shifts. In 1995, youngest sister Dai Wei Li co-founded Marvell with her husband Sehat Sutardja and his brother, focusing on storage controllers. Eldest brother Dai Wei Min founded EDA company Ultima, later sold to Cadence, and later founded VeriSilicon (芯原) in China, becoming a leading semiconductor IP provider. Second brother Dai Wei Jin co-founded EDA firm Silicon Perspective (sold to Cadence) and GPU IP company Vivante, later acquired by VeriSilicon. The combined "Dai-Sutardja" family network extends beyond Marvell. Their ventures and investments form a comprehensive ecosystem for the post-Moore's Law, chiplet era. Key holdings include: Dream Big Semiconductor (AI SuperNICs, acquired by Arm), Alphawave (high-speed SerDes IP, acquired by Qualcomm), and Silicon Box (a chiplet advanced packaging foundry). VeriSilicon itself thrives on the AI ASIC and IP boom in China. Collectively, the family's AI infrastructure-related portfolio is estimated at over $22 billion. Their strategy represents a distinct path: building critical components for open standards and key manufacturing capacity in the chiplet era, rather than pursuing standalone AI chip dominance. While this path may not create the next NVIDIA, it has enabled repeated successful exits and sustained influence within the global semiconductor industry.

marsbit4 ч. назад

After Marvell's 32% Surge, the Chinese Chip Family Behind It Emerges

marsbit4 ч. назад

CPU, Quietly Returning to the Center of the AI Computing Power Stage

Over the past three years, AI computing power narratives have been dominated by GPUs. However, starting in 2026, this story began to shift. While training large models remains GPU-intensive, the rapid growth of inference and AI agent workloads, which require high levels of task orchestration, concurrency, and data flow management, has highlighted a renewed critical role for CPUs. These are tasks GPUs are not designed to handle. Intel's recent launch of the Xeon 6+ processor, built on its Intel 18A process and featuring up to 288 efficiency cores (E-cores), exemplifies this strategic pivot. It is positioned not as a mere companion to GPUs but as the essential "control plane" for AI infrastructure, optimized for high-density, energy-efficient, and high-throughput workloads characteristic of AI agents and inference. This "CPU resurgence" is not about CPUs outperforming GPUs in raw computation. It reflects a systemic bottleneck: as AI scales from training single models to deploying countless intelligent agents, the demand for coordination and data handling surges. Major cloud providers are also developing their own high-density ARM-based server CPUs for similar workloads. However, Intel's success with this strategy faces significant challenges. Competition includes NVIDIA's integrated CPU-GPU solutions, the expanding adoption of cloud vendors' in-house ARM CPUs, and the crucial market test of Intel's 18A manufacturing process against rivals like TSMC's N2. In conclusion, CPUs are indeed reclaiming a central, though redefined, role in AI compute—managing the complex orchestration that enables massive-scale AI deployment. While the trend is clear, which company will ultimately lead this CPU resurgence remains an open question to be decided in the data centers of 2027 and beyond.

marsbit5 ч. назад

CPU, Quietly Returning to the Center of the AI Computing Power Stage

marsbit5 ч. назад

Jensen Huang's 2026 GTC Taipei Speech: The Era of AI Agents is Here, Computing is Revenue

NVIDIA CEO Jensen Huang's 2026 GTC Taipei speech announces the arrival of the "Agent AI" era, where AI transitions from content generation to performing useful work. Huang positions tokens as units of profit and GDP, driving massive demand for computing power and "AI factories." NVIDIA's strategy revolves around a new computing paradigm centered on AI agents, which combine large language models (LLMs) with agent frameworks for planning, memory, and tool use. Key announcements include: * **Vera Rubin:** A complete, end-to-end system (not just a GPU) designed from the ground up to run AI agents at scale, representing NVIDIA's evolution into an infrastructure company. * **Vera CPU:** A revolutionary CPU architecture built specifically for impatient AI agents, prioritizing low latency, single-thread performance, and massive bandwidth over traditional multi-core throughput. * **Enterprise AI Agent Toolkit:** A suite including open models (like Nemotron 3 Ultra), frameworks, tools, and a secure runtime (Open Shell) to enable every company to build and deploy its own AI agents. * **Next-Gen PCs with Microsoft:** A new line of Windows desktops, laptops, and workstations co-developed with Microsoft, featuring the N1X chip and designed to run local AI agents, redefining the personal computer. * **Physical AI Foundation Models:** Introduction of Cosmos 3 for robotics and physical AI, Alpamayo 2 for autonomous driving, and the Isaac GR00T platform—a fully integrated humanoid robot reference system. Huang emphasizes that the same core agent computing pattern (model + framework + tools + runtime) will extend from the cloud and PCs to robots, factories, and edge devices. He concludes that the industry is fundamentally changed as useful, agentic AI creates a vast new market where "compute is revenue."

marsbit12 ч. назад

Jensen Huang's 2026 GTC Taipei Speech: The Era of AI Agents is Here, Computing is Revenue

marsbit12 ч. назад

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