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.orgThis 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.
Compiled based on PitchBook’s “Q2 2026 Building, Backing, and Buying AI”: Against the backdrop of AI infrastructure spending in 2026 approaching $600 billion, Google, Microsoft, Amazon, and Meta are more inclined to build computing capacity and make strategic minority equity investments rather than acquire AI capabilities through mergers and acquisitions. This shift is reshaping AI startup exit paths, enterprise software competition, and capital allocation in AI infrastructure.
The iTnews and NEXTDC State of Data & AI event in Sydney reflected how the focus of enterprise AI discussions is shifting from proof of concept to scaled deployment, data sovereignty, compliance, and Agentic AI. For enterprises, the real question is no longer “whether to use AI,” but “how to build a sustainable path to implementation between infrastructure, governance, and business value.”
This article is based on an industry perspective on “evaluation data (eval data)” and analyzes why AI competition is shifting from model capability to workflows, user access, and feedback loops, while also discussing its impact on enterprise AI, AI agents, AI platforms, and the industry landscape.