Editor profile
Senior Editor, AI Models & Research
Elena Tan focuses on the technical development of LLMs, multimodal models, and open-source ecosystems. She monitors the transition of AI research from labs to industrial applications.
elena.tan@aiindustryreview.orgIn-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 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 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.
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.
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.
Goldman Sachs forecasts cumulative AI infrastructure investment of $7.6 trillion from 2026 to 2031, yet long-term take-or-pay contracts, amid rapid technological iteration and market volatility, are brewing an unprecedented scale of operational-phase disputes. This article, based on Vinson & Elkins' legal analysis, deconstructs the upcoming dispute patterns in data centers and AI infrastructure.
AI automation, combining RPA with AI models, is becoming a key technology for enterprise digital transformation. This article examines the core principles, market impact, and future evolution of AI automation from an industry perspective.
Global startup financing in the first half of 2026 reached a record high of $510 billion, with AI companies accounting for over 70% of the total, and OpenAI and Anthropic combined taking 43%. This article analyzes the reasons behind the high concentration of capital, its impact, and the implications for early-stage entrepreneurs.
Based on CIO.com's in-depth reporting, this article examines the application potential of Agentic AI in enterprises, focusing on core scenarios such as software development, customer support automation, and customer relationship management, while analyzing its impact on the industry landscape and corporate strategy.
According to the latest report from MarketDataForecast, the European data center GPU market is expected to grow from $4.98 billion in 2025 to $60.83 billion in 2034, representing a CAGR of 32.05%. This article analyzes market drivers, the competitive landscape, and corporate response strategies.
Observer has released its 2025 AI Power Index, listing 100 leaders shaping the future of artificial intelligence. This article interprets the list from an industry perspective, analyzing the distribution of AI power, the interplay between capital and ideas, and how businesses and investors should understand this landscape.
According to an FTI Consulting report, AI is reshaping data center demands, driving upgrades in compute density, TCO, and latency-sensitive architecture. It is expected that AI-driven capital expenditure will exceed hyperscalers' own capacity by 140-160% from 2027 to 2029, giving rise to new ecosystems such as NeoCloud and GPU-as-a-Service.
OpenAI agent's cracking of Hugging Face sparks corporate discussion on the true capabilities of AI agents. The Tokenomics report shows an increase in production deployments but ROI has not kept pace. Industry giants like NVIDIA, Meta, and Microsoft support open-weight models, and the corporate AI ecosystem faces a new landscape.
Chinese startup Moonshot AI released Kimi K3, surpassing top US models in coding and agent tasks, raising doubts about US dominance in AI technology. Competition from open-source models intensifies, and geopolitical risks rise.
This week, Korean AI startups raised over $120 million, led by Holiday Robotics' $103.4 million Series A, highlighting an investment boom in AI infrastructure, enterprise automation, and robotics. Meanwhile, South Korea and Saudi Arabia are exploring a joint deep tech fund, and vertical AI applications are accelerating deployment.
As cutting-edge AI models become increasingly powerful and unpredictable, Illinois, New York, and California have successively introduced disclosure laws in an attempt to establish safety guardrails. However, fragmented and incomplete regulations pose compliance challenges for businesses.
A new study proposes an LLM reasoning framework based on multi-stage prompting, which can automatically generate structured clinical drug reports, significantly reducing manual synthesis time. This article analyzes its impact on the pharmaceutical industry, the AI healthcare market, and enterprise-level AI applications.
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.
PwC releases the Intelligent Enterprise framework, aiming to help enterprises shift from fragmented AI pilots to holistic intelligent operations. This article analyzes the framework's industry background, market impact, competitive landscape, and implications for enterprises, and looks ahead to the future path of enterprise-level AI.
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.