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In-depth analysis of the scale, drivers, and regional distribution of the global generative AI market, exploring the impact of large language models, enterprise application implementation, investment hotspots, and regulatory environment on the industry structure.
In-depth analysis of the regulatory challenges brought about by the rapid development of AI technology, exploring the theoretical foundations, main risk points, and implications for global enterprises and policymakers in building an international coordinated governance framework.
In-depth analysis of key bottlenecks for enterprise generative AI from model capabilities to actual business implementation, focusing on AI infrastructure, Agent applications, data governance, and the impact of regulatory policies on enterprise ROI.
In-depth analysis of how Amazon utilizes cutting-edge technologies, from Supervised Fine-Tuning (SFT) to GRPO, through fine-grained model tuning, to transform LLMs from general models into highly reliable multi-agent systems, solving performance and accuracy challenges in high-risk applications.
In-depth analysis of China's self-reliance strategy in the artificial intelligence technology stack (chips, models, applications). Discuss how the national level guides industrial development through capital and policy, and the key strategic competition in the technological rivalry with the United States.
In-depth analysis of the capital expenditure scale of AI infrastructure construction, exploring the profound impact of computing hardware, electricity, and geopolitics on the global data center market, providing industry perspectives for investors and corporate decision-making.
In-depth analysis of Mary Meeker's report on the AI economy, from giant capital expenditure and model data requirements to open-source versus closed-source competition, providing business decision-makers with a macro perspective on the AI industry.
In-depth analysis of the fragmented landscape of global AI regulation, from the EU AI Act to Chinese administrative measures, providing industry insights into compliance challenges and future development paths across jurisdictions for business decision-makers.
In-depth analysis of Mary Meeker's report on the AI industry, from corporate capital expenditure and model data requirements to AI infrastructure challenges, open-source versus closed-source competition, and the US-China competitive landscape, providing industry insights for business decision-makers and investors.
A policy brief from Harvard's Belfer Center shows that U.S. data center electricity consumption could rise to 325–580 terawatt-hours by 2028, accounting for 6.7%–12.0% of national electricity use. The debate over who pays for grid upgrades is reshaping the siting, financing, and regulatory logic of AI infrastructure.
The generative AI market size is expected to grow from USD 161.0 billion in 2026 to USD 1,260.15 billion in 2034, at a compound annual growth rate of 29.30%. North America leads with a 48.70% share, while Asia Pacific becomes the fastest-growing region. This article, based on a Fortune Business Insights report, provides insights into enterprise AI adoption trends, industry landscape, and investment directions.
A study published in Scientific Reports proposes a reward-updated GRPO framework that uses LLMs to generate reasoning data for training another model, achieving high domain accuracy at a cost of approximately $80. This article analyzes the impact of this technology on enterprise AI customization, the open-source ecosystem, and infrastructure from an industry perspective.
The EU AI Act, China's Generative AI Measures, and U.S. state-level legislation—global AI regulation is rapidly fragmenting. How can companies get ahead in multi-jurisdictional compliance? Drawing on the AI regulatory tracker report published by White & Case, this article reviews the trends and offers recommendations for response.
Eversheds Sutherland’s July 2026 “Global AI Regulatory Update” shows that global regulatory focus is shifting toward transparency requirements, with corporate compliance costs and strategic risks rising in tandem.
Based on the MERICS report, this provides an in-depth analysis of China's self-reliance efforts across the entire industry chain of AI chips, machine learning frameworks, and large language models, as well as their market impact and implications for the global industry landscape.
A frontier review points out that competition among large language models is shifting from a race for scale to full-lifecycle engineering, with data quality, evaluation capability, and safety alignment replacing parameter scale as the new determining factors.
Observer releases the 2025 AI Power Index ranking. This article analyzes the capital and ideological competition behind the ranking from an industry perspective, revealing the concentration of power and geopolitical competition in the AI industry.
The 2026 World Artificial Intelligence Conference (WAIC) opened in Shanghai, with 29 countries signing the founding agreement of the World AI Cooperation Organization (WAICO), as the AI governance framework promoted by China runs parallel to the Western system. This article analyzes the deep impact of this event on the global AI industry, corporate compliance, computing infrastructure, and the competitive landscape, and explores the industry signals behind Huawei's Atlas 950 and AI Agent phones.
A recent study in Scientific Reports proposes using large models to generate reasoning data and enhancing another LLM's reasoning ability by updating the GRPO reward mechanism, with a training cost of only about $80. This article interprets its impact on AI training efficiency and enterprise applications from an industry perspective.
Based on the 2025 AI Power Index released by Observer, this provides an in-depth analysis of the global AI industry's power structure, the interplay between capital and ideas, and the implications for businesses and investment institutions.
White Jade Law Firm published the "AI Watch: Tracking US Regulation" report, reviewing federal and state-level AI legislative developments and analyzing the profound impact of fragmented US AI regulation on enterprise-level AI deployment.
A Columbia University report proposes an AI power gap index, which comprehensively measures the shaping power of tech giants, governments, open-source communities, and other entities on the AI ecosystem, revealing a trend of industrial power concentration.
Enterprise software is undergoing the biggest architectural transformation since SaaS, with intelligent agent applications shifting from record systems to outcome systems, automating complex business processes, and improving efficiency and decision quality. Taking Oracle Fusion as an example, it analyzes market impact, competitive landscape, and business insights.
NVIDIA launches RoboLab simulation benchmark platform, addressing key issues in current robot policy evaluation such as visual domain overlap, benchmark saturation, insufficient diagnostics, and low statistical confidence, providing a set of robot-agnostic, rapidly generable task analysis tools to advance general robot policies toward real-world deployment.
A study published in npj Digital Public Health systematically evaluated the performance of five mainstream LLM architectures in simulating vaccination decisions, revealing significant biases among models, with some exhibiting a pro-science tendency. This finding has important implications for AI applications in public health modeling, corporate decision simulation, and other scenarios.
Deloitte's "2026 Global Sports Industry Outlook" points out that AI is transforming sports operations, capital structures, and media convergence. This article analyzes the application, market impact, and future trends of AI in the sports industry chain.
The Trump administration imposed an export ban on Anthropic's latest model, raising concerns about transparency in AI regulation. This article analyzes the current state of U.S. AI regulation, market impact, and industry implications.
As enterprise AI moves from experimentation to production, GPUs are not the only bottleneck. Data delivery efficiency is becoming a key factor in determining the return on AI investment, and enterprises need to re-evaluate the infrastructure between storage and computing.
This week, the Korean AI startup ecosystem shows that corporate strategic investments are shifting from consumer-grade to industrial-grade AI infrastructure. AIM Intelligence has received investments from giants like LG and Hyundai; medical AI commercialization is accelerating, with Evom AI securing funding after regulatory approval; the government is promoting industrial policies such as the K-Food Smart Manufacturing Alliance.
Microsoft has released the open-source framework ASSERT, which uses natural language descriptions to turn expected AI behavior into executable tests, reflecting that enterprise AI is moving from a “model capability race” into a “application behavior verification” stage.