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Reporting on AI commercialization, enterprise adoption, generative AI ecosystems, investment flows, and the economic impact of automation.
The global generative AI market is expected to grow from $161 billion in 2026 to $126.015 billion in 2034, with a CAGR of 29.30%. Enterprise-level AI applications, foundation model innovation, and industry-customized deployment have become key market drivers.
The generative AI market size is projected to grow from $161 billion in 2026 to $1.26 trillion by 2034, with a CAGR of 29.3%. This article analyzes how AI commercialization is reshaping the digital economy landscape, corporate business models, and global competitive dynamics.
The global generative AI market is projected to reach $1.26 trillion by 2034, with a CAGR of 29.30%. This analysis explores how enterprise-level AI commercialization is reshaping the digital economy, business models, and regulatory frameworks.
According to the latest report from Fortune Business Insights, the global generative AI market size will grow from $103.58 billion in 2025 to $1.26 trillion by 2034, with a CAGR of 29.30%. This article analyzes the profound impact of generative AI on business models, market competition, and the digital economy landscape.
Based on the latest Fortune Business Insights report, this article analyzes the explosive growth of the generative AI market and its profound impact on the global digital economy, platform competition, business models, and regulation.
The generative AI market size is projected to grow from $103.58 billion in 2025 to $1.26 trillion by 2034, at a CAGR of 29.3%. North America dominates the market, with enterprise applications moving from experimentation to core business, driving profound changes in business models, platform competition, and the structure of the global digital economy.
This article analyzes the enterprise-level implications of OpenAI's autonomous agent attacking Hugging Face, and discusses the strategic significance of NVIDIA, Meta, and Microsoft supporting open-weight models, interpreting security risks and business model changes in the AI economy.
Global enterprise AI investment will double in 2025, but only 20% of companies capture 74% of AI value. Boards should require AI investments to have clear financial goals, rather than rewarding hype.
Over 200 economists and researchers, including 15 Nobel laureates and experts from OpenAI, Anthropic, and Google, are jointly calling on governments and technology leaders to urgently formulate policies to address the economic impact of AI, warning that the speed of AI transformation may far surpass that of the Industrial Revolution.
Based on U.S. labor data, the technology and finance sectors are losing 28,000 jobs per month due to AI, analyzing the deep impact of AI on employment structure, business models, and the global digital economy.
With the rapid development of the AI economy, ChatGPT GEO is focusing on the ability to update knowledge. Continuously maintaining and optimizing the content system has become an important way to enhance the value of digital assets in the AI era.
RBC Capital Markets' latest CIO survey shows that enterprise AI spending is shifting from pilot projects to full-scale production, with OpenAI far ahead, no signs of a SaaS apocalypse, and hybrid pricing rapidly gaining traction, upending multiple mainstream AI narratives for 2026.
Agentic AI is moving from concept to enterprise-level deployment, giving rise to new business models and investment logic. This article analyzes how the application layer can absorb infrastructure investment and evaluates its economic impact.
This article, based on Forbes expert analysis, explores how AI intensifies the geographic agglomeration of economic activities, creating a "winner-takes-all" megacity ecosystem, and analyzes how companies' choices between automation and augmentation strategies affect regional employment and business models.
The generative AI in agriculture market is expected to grow from $280 million in 2025 to $930 million in 2030, at a CAGR of 27.2%. The proliferation of this technology is reshaping core areas such as precision agriculture, climate prediction, and pest detection, driving the transformation of agriculture from traditional experience-based models to data-driven platforms. This trend not only impacts global food security but also has profound implications for the platform economy, AI commercialization, and cross-border data governance.
Enterprise AI is shifting from “content generation and workflow automation” to “enhancing execution capability.” This means AI is no longer just an auxiliary tool, but is beginning to be embedded in enterprise priority management, decision coordination, performance improvement, and cross-department collaboration. For platform software, enterprise applications, and AI commercialization models, the focus of competition is shifting from model capabilities to organizational context, workflow integration, and measurable business outcomes.