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In-depth analysis of how Agentic AI drives fundamental paradigm shifts in enterprise platforms, exploring its impact on AI infrastructure, enterprise applications, and capital markets.
In-depth analysis of the fragmented trend of global AI regulation, from the EU AI Act to the differences in regulation between China and the US, exploring the compliance risks and strategic opportunities enterprises face in different jurisdictions.
In-depth analysis of the explosive growth of India's AI data center infrastructure, exploring the key role of GPU supply and advanced cooling technology in driving the implementation of global AI applications and their impact on industry competition.
In-depth analysis of the shift in the development stage of current large language models (LLMs). The article points out that the competition for model scale has shifted to engineering challenges such as data quality, reliable evaluation, and model alignment safety, which has a profound impact on the practical application of enterprise AI.
In-depth analysis of how AI automation surpasses traditional RPA by combining LLM and Agent technologies to achieve autonomous planning and decision-making of enterprise processes, exploring its disruptive impact on enterprise efficiency, cost, and competitive landscape.
In-depth analysis of how China's AI ecosystem competes with leading US models through open-source innovation and cost-efficiency pathways, forming two parallel development tracks. Discuss the competitive landscape and investment opportunities at the model, application, and infrastructure levels.
In-depth analysis of the fragmented landscape of global AI regulation, from the EU AI Act to country-specific approaches, providing strategic reference for business decision-makers in navigating complex compliance environments.
In-depth analysis of the business model, capital flows, and drivers of computing power deployment for GPU as a Service (GPUaaS) in the AI infrastructure field. Exploring the contract, capital, and computing ecosystem behind the prosperity of AI infrastructure.
In-depth analysis of Mary Meeker's AI trend report, providing decision-makers with a macro view of the AI industry, covering corporate capital expenditure, model data requirements, computing power competition, and the landscape changes in open-source and closed-source ecosystems.
Analyze how Singapore can transition AI from tool exploration to large-scale, trustworthy real-world deployment through global collaboration, focusing on physical AI, infrastructure, and enterprise implementation strategies.
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 the global demand for high-performance computing infrastructure from AI, focusing on the explosive growth forecast of the high-bandwidth memory (HBM) market from 2025 to 2034, and analyzing its profound impact on AI enterprise applications and the semiconductor industry.
In-depth analysis of how enterprise AI solutions evolve from efficiency tools to core strategic assets. Discuss the actual ROI, infrastructure requirements, and governance challenges of key applications such as AI Agents, customer service, and sales automation in enterprise operations.
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 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 how Agentic AI can push generative AI from content creation towards real-time decision-making and complex workflow automation, exploring its practical implementation paths and business value in enterprise applications such as software development, RPA, and customer support.
In-depth analysis of how enterprise AI solutions transcend the scope of tools, reshaping business operations through core applications such as customer support, sales automation, and data analytics, and exploring key considerations for enterprises when choosing and deploying these complex AI systems.
This article deeply analyzes the complete lifecycle of end-to-end AI model training and fine-tuning, covering data preparation, model training, human feedback alignment (RLHF/DPO), and practical paths for multimodal AI, providing industry implementation perspectives for enterprises and investors.
Based on Mary Meeker's in-depth report, analyze the capital expenditure drivers, model scale competition, energy challenges, and the competitive landscape between open-source and closed-source in the AI industry to provide industry perspectives for business decision-makers.
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 the scale forecast, driving factors, key application scenarios, and challenges facing the US AI data center market. Analyze how this market will achieve exponential expansion through a compound annual growth rate of 27.4% from 2026 to 2032.
In-depth analysis of the key transformation from pilot to scale for enterprise AI applications, exploring the profound impact on corporate strategy, talent, and infrastructure from productivity enhancement, business reshaping, and AI governance.
In-depth analysis of the structural impact of the emergence of China's new open-source large model GLM-5.2 on the global AI ecosystem, open-source vs. closed-source competition, and the implementation of enterprise AI applications, exploring new dimensions of Sino-US AI competition.
In-depth analysis of the engineering phase of large language model (LLM) development. This paper elaborates on three core bottlenecks—data quality, model evaluation, and alignment safety—arguing that the focus of future AI competition will shift from merely pursuing scale to building reliable and trustworthy AI systems.
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.
AI marketing platform Profound has raised another $180 million Series D just seven months after completing a $96 million Series C, lifting its valuation to $1.8 billion. Sequoia and Kleiner Perkins co-led the round. This article analyzes the commercialization path in the AI search optimization sector, agentic product structures, and the impact on enterprise procurement decisions.
Precedence Research's latest forecast shows that the global generative AI server market will grow from US$101 billion in 2025 to approximately US$1.885 trillion in 2035, with a compound annual growth rate of 34%. This article analyzes the industrial implications of this structural reconstruction of computing infrastructure from four dimensions: supply chain, custom silicon competition, power constraints, and enterprise procurement strategies.
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.