Editor profile
Editor, Enterprise AI & Implementation
Julian Chen investigates the practical implementation of AI in enterprise workflows and automation. He reports on ROI analysis and the digital transformation of traditional industries through AI.
julian.chen@aiindustryreview.orgThe 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.
According to the latest report from SNS Insider, the vertical-domain large language model market is expected to grow from $4.56 billion in 2025 to $88.45 billion in 2035, with a CAGR of 34.55%, reflecting the evolution of enterprise-level AI toward deep industry customization.
Meta releases a new AI model and reiterates its open-source commitment, with Zuckerberg's declaration drawing industry attention. This article analyzes the profound impact of open-source AI on the enterprise market, competitive landscape, and future trends.
Based on an in-depth analysis of CybertizeWeb's "Global LLM Ecosystem Report," this provides a data-driven reference for industry decision-makers by interpreting the global large model market size, competition between open-source and closed-source, enterprise adoption rates, pricing trends, and multimodal evolution.
Based on White & Case's latest AI regulatory tracking report, this analysis examines the fragmented landscape of parallel US federal and state-level AI legislation, and its differentiated impact on tech giants, startups, and investment institutions. How can companies turn regulatory pressure into compliance competitiveness?
Deloitte releases its annual "2026 Technology Trends" report, interpreting enterprise AI applications, infrastructure investment, and competitive landscape changes from the perspective of the AI industry, and analyzing their implications for decision-makers and investors.
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.
This article is based on the AWS official blog, analyzing the effectiveness of Amazon's application of advanced fine-tuning technology in three major scenarios—healthcare, engineering, and e-commerce—and exploring the competitive landscape and future trends of enterprise AI in the era of multi-agent orchestration.
This article analyzes the fragmented state of US AI regulation and explores the compliance challenges and response strategies faced by enterprises.
As the United States imposes export controls on Anthropic's models, AI sovereignty has become an unavoidable strategic issue for enterprises. This article analyzes full-stack control, emerging market response strategies, and the trend of global regulatory divergence, providing decision-making references for businesses.
Nature published a perspective article, proposing that understanding large language models requires distinguishing between human projection and machine cognition, and that the framework of machine empiricism may change the logic of R&D, investment, and evaluation in the AI industry.
While the industry focuses on cutting-edge models, open-source models have quietly taken over large-scale production workloads. According to Hugging Face data, Chinese open-source models account for 41%, and enterprises are turning to building their own models to avoid vendor lock-in.
From a single transaction to a comprehensive ecosystem, BitVulpex releases a panoramic report on its second anniversary, systematically reviewing the platform’s achievements and progress across four dimensions: product line expansion, AI technology application, compliance system establishment, and asset security protection.
KPMG Technology Lead Phil Wong stated that Agentic AI and inference workloads will drive demand for high-bandwidth, low-latency fiber optic connections, while power supply becomes the biggest bottleneck for AI infrastructure expansion.
The scale of AI infrastructure investment has reached $750 billion, with NVIDIA, Alphabet, and Oracle occupying key positions in the industry chain through different strategies. This article analyzes the business models, financial performance, and market risks of the three companies, providing an industrial perspective for corporate decision-makers and investors.
As AI usage costs skyrocket, enterprises shift from pursuing the most powerful models to prioritizing cost-effectiveness, ushering in opportunities for open-source and domestic models.