Editor profile
Senior Editor, AI Infrastructure & Computing
Marcus Vance covers the hardware stack of the AI era, including GPUs, data centers, and compute networks. He analyzes the supply chain dynamics of high-performance computing resources.
marcus.vance@aiindustryreview.orgEnterprise software is undergoing the biggest architectural transformation since SaaS, with intelligent agent applications shifting from record systems to outcome systems, automating complex business processes, and improving efficiency and decision quality. Taking Oracle Fusion as an example, it analyzes market impact, competitive landscape, and business insights.
Analyze the progress Meta has made in rebuilding its AI organization after the failure of Llama 4, focusing on the triple advantages of data, talent, and computing, and their impact on the competitive landscape of the AI industry.
This article focuses on the debate between AI context and real-time organizational truth, analyzing how enterprises can reduce their reliance on cutting-edge large models through context engineering and intelligent control to achieve sustainable AI implementation. It also explores new standards for measuring AI value—shifting from usage rates to business outcomes.
Nexdata will showcase four major AI data solutions covering GenAI/VLM, Physical AI, SpeechLLM, and LLM at ICML 2026, highlighting the key role of high-quality data in model training and deployment, with the industry focusing on data infrastructure investment.
Based on analysis from Bloomberg Law, this article explores how enterprises can use a four-dimensional framework (geography, industry, stakeholder roles, and risk categories) to address increasingly complex AI regulations, and uses California regulations as an example to demonstrate the practical application of the framework.
This week, the Korean AI startup ecosystem shows that corporate strategic investments are shifting from consumer-grade to industrial-grade AI infrastructure. AIM Intelligence has received investments from giants like LG and Hyundai; medical AI commercialization is accelerating, with Evom AI securing funding after regulatory approval; the government is promoting industrial policies such as the K-Food Smart Manufacturing Alliance.
Starting from iTnews' case, analyze why enterprises still frequently encounter project implementation failures after introducing AI, and discuss the impact of governance, organization, and infrastructure on AI commercialization.
As AI chips continue to increase power density, data centers are shifting from “computing power server rooms” to “power engineering projects.” This infrastructure overhaul is affecting not only NVIDIA, hyperscale cloud providers, and the power equipment supply chain, but is also changing the cost structure, deployment pace, and regulatory pressure of AI commercialization.