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Artificial intelligence news, AI products, model updates, automation, AI safety, regulation, infrastructure, and the practical impact of AI on software, security, work, and everyday technology.

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SoftBank SB Neo Turns AI Cloud Capacity Into a 10-Gigawatt Race

SoftBank has formed SB Neo, a U.S.-based neocloud company meant to supply AI chips and cloud services to model developers and large enterprises. The plan, tied to SoftBank's 10-gigawatt AI infrastructure target by 2030, shows how AI compute is shifting from scarce GPU rental toward vertically managed infrastructure businesses built around power, chips, networking, and operations.
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Alibaba’s Claude Code Ban Turns AI Coding Tools Into a Vendor-Risk Test

Alibaba will reportedly bar employees from using Anthropic’s Claude Code in workplace environments starting July 10 after concerns over hidden anti-abuse fingerprinting inside the coding tool. The dispute shows why companies adopting AI coding agents now need to audit vendor controls, client behavior, regional restrictions, and data handling with the same seriousness they apply to any privileged developer software.
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Microsoft Surface devices showing Windows and Microsoft Copilot experiences in an office setting

Microsoft Frontier Company Turns Enterprise AI Into an Embedded Engineering Race

Microsoft is putting $2.5 billion and 6,000 industry and engineering experts behind Microsoft Frontier Company, a new operating business meant to help customers turn AI pilots into production systems. The move follows AWS, OpenAI, and Anthropic into embedded enterprise AI work, where the hard part is no longer access to models but making agents, data, governance, and workflows actually function inside large companies.
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Laptop screen showing code at a developer workstation

OpenAI Fine-Tuning Cutoff Puts Custom AI Projects on a Migration Clock

OpenAI’s July 2 fine-tuning cutoff blocks new training jobs for organizations that have not recently used fine-tuned models. Existing deployed fine-tunes are not being shut off immediately, but developers now have a clear deadline to audit custom models, preserve active projects, and decide whether prompts, retrieval, tools, or another training path should replace self-serve fine-tuning.
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