SpaceX to Acquire Cursor 30 Days After IPO

SoftBank announced a U.S. neocloud venture on Thursday, adding to hundreds of firms now competing in the AI server rental market. Together AI raised $800 million at an $8.3 billion valuation, while Nvidia said it will provide financial backing to younger cloud firms in exchange for a revenue share. The moves highlight intense competition in the sector, though Nvidia's backstop offer raises questions about the actual strength of demand for computing capacity.

Anthropic is in early-stage talks with Samsung Electronics to manufacture a custom AI chip, according to sources with direct knowledge of the project. The move mirrors OpenAI's strategy of developing proprietary chips to reduce dependence on external computing infrastructure and control costs. Google, Amazon Web Services, Meta, and Microsoft have all developed their own chips, while OpenAI unveiled Jalapeno, an inference chip designed for large-language models, last month.

Model routers, which automatically select the most cost-effective AI model for a given task rather than defaulting to expensive cutting-edge options, are gaining adoption among enterprises seeking to reduce AI spending. Companies like Snowflake and Palo Alto Networks have reported cost savings by routing basic tasks such as email summarization and document search to cheaper open source or older proprietary models. The routers take multiple forms, from standalone products to cloud provider features to internal IT-built applications, all aimed at maintaining quality while lowering costs as organizations grapple with rising AI model prices and employee overuse of premium models.

Microsoft has launched a dedicated AI deployment company backed by a $2.5 billion commitment, joining Amazon, OpenAI, and Anthropic in establishing specialized units focused on AI implementation. The move signals Microsoft's intent to build infrastructure and services around enterprise AI adoption. The company follows a pattern of major tech firms creating separate entities to handle AI deployment at scale.