Optical compute interconnect consortium targets open artificial intelligence infrastructure standard

A new optical compute interconnect consortium led by major chip and cloud companies aims to define an open specification for scale-up links in modern artificial intelligence infrastructure, shifting away from copper limitations.

The Optical Compute Interconnect Multi-Source Agreement group has been formed as an industry consortium led by founding members AMD, Broadcom, Meta, Microsoft, NVIDIA and OpenAI. The group is focused on creating an open specification for optical scale-up interconnects to support a hyperscaler-driven open ecosystem. By coordinating around an open standard, the consortium aims to enable a multi-vendor supply chain for optical compute interconnects across artificial intelligence data centers.

The consortium positions this effort as a foundational move for the next generation of artificial intelligence infrastructure. By aligning on an open specification, the Optical Compute Interconnect Multi-Source Agreement members are promoting what they describe as a robust optical ecosystem. The goal is to ensure that the future of artificial intelligence interconnects is built on a flexible, multi-vendor foundation, allowing operators to mix and match components from different suppliers while maintaining interoperability for optical interconnect needs.

The group frames its work around a physics and power mandate as large language models advance toward super intelligence and demand more scalable compute fabrics. As large language models advance toward super intelligence, traditional copper-based connectivity is reaching limitations in physical reach which are impacting artificial intelligence cluster scale-up domain architectures. The consortium states that Optical Compute Interconnect will enable migration from copper-based to optical-based scale-up architectures, with the intention of alleviating copper interconnect bottlenecks in high-performance artificial intelligence clusters.

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Building a strong data infrastructure for artificial intelligence agents

Enterprises are rapidly experimenting with agentic artificial intelligence, but most struggle to scale because their data architectures lack the business context and trust needed for reliable outcomes. A semantic, business-aware data layer is emerging as the critical foundation for effective agents that work alongside, not instead of, existing software systems.

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