SK Group and AWS are partnering to build South Korea’s largest AI data center in Ulsan with a $5.13 billion investment. The facility will launch with 60,000 GPUs and 103 MW capacity, expanding to 1 GW, creating up to 78,000 jobs. This milestone boosts South Korea’s AI leadership, data sovereignty, and positions Ulsan as a major AI hub in Asia.
This article critiques the common practice of exhaustive data cleaning before implementing AI, labeling it a consultant-driven “scam.” Data cleaning is a never-ending and expensive process, delaying AI implementation while competitors move forward. Instead, I champion a “clean as you go” approach, emphasizing starting with a specific AI use case and cleaning data only as needed. Smart companies prioritize iterative improvement by using AI to fill in data gaps and building safeguards around imperfect data, ultimately achieving faster results. The core message is it’s more important to prioritize action over perfection, enabling quicker AI adoption and thereby competitive advantage.
Private 5G in South Korea is reshaping manufacturing by enabling autonomous robots, real-time quality control, and AI-driven operations. This video podcast explains how dedicated spectrum, practical deployment models, and flexible pricing help factories justify Private 5G over Wi-Fi, plus insights on scaling ROI and future AI-native networks.
Edge AI is reshaping broadband customer experience by powering smart routers, proactive troubleshooting, conversational AI, and personalized Wi-Fi management. Learn how leading ISPs like Comcast and Charter use edge computing to boost reliability, security, and customer satisfaction.
The pressure to adopt artificial intelligence is intense, yet many enterprises are rushing into deployment without adequate safeguards. This article explores the significant risks of unchecked AI deployment, highlighting examples like the UK Post Office Horizon scandal, Air Canada’s chatbot debacle, and Zillow’s real estate failure to demonstrate the potential for financial, reputational, and societal damage. It examines the pitfalls of bias in training data, the problem of “hallucinations” in generative AI, and the economic and societal costs of AI failures. Emphasizing the importance of human oversight, data quality, explainability, ethical guidelines, and robust security, the article urges organizations to
A global IBM study reveals 81% of CMOs see AI as critical for growth, yet 54% underestimated the operational complexity. Only 22% have set clear AI usage guidelines, despite 64% now being responsible for profitability. Siloed systems, talent gaps, and lack of collaboration hinder translating AI strategies into results, highlighting a major execution gap as marketing leaders adapt to increased accountability for profit and revenue growth.
Elon Musk’s generative AI firm, xAI, is targeting $4.3 billion in new equity funding, following its previous $6 billion raise and a $5 billion debt effort. The capital will support high-cost AI models like Grok and Aurora, expand massive GPU-powered data centers, and drive xAI’s ambition to compete with leaders like OpenAI and DeepMind. Investors remain interested despite concerns over spending, betting on Musk’s strategy to blend social media and AI under one ecosystem.
The emergence of 6G networks marks a paradigm shift in the way wireless systems are conceived and managed. Unlike its predecessors, 6G will embed Artificial Intelligence (AI) as a native capability across all network layers, enabling real-time adaptability, intelligent orchestration, and autonomous decision-making. This paper explores the symbiosis between AI and 6G, highlighting key applications such as predictive analytics, alarm correlation, and edge-native intelligence. Detailed insights into AI model selection and architecture are provided to bridge the current technical gap. Finally, the cultural and organizational changes required to realize AI-driven 6G networks are discussed. A graphical abstract is suggested to
Telecom engineers know OSS systems aren’t broken—they just pretend to work. Outdated data, broken integrations, and overwhelming alerts create false confidence and slow operations. Discover how VC4’s Service2Create delivers real-time, trusted inventory and smarter workflows that engineers can actually rely on.
As the telecom world accelerates toward 5G-Advanced and sets its sights on 6G, artificial intelligence (AI) is no longer a peripheral technology — it is becoming the brain of the mobile network. AI-driven Radio Access Networks (RANs), and increasingly AI-native architectures, are reshaping how operators design, optimize, and monetize their networks. From zero-touch automation to intelligent spectrum management and edge AI services, the integration of AI and machine learning (ML) is unlocking both operational efficiencies and new business models.
This article explores the evolution of AI in the RAN, the architectural shifts needed to support it, the critical role
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