The whole AI stack moves faster in the open

Nebius argues that although modern AI relies heavily on open-source software, data, research, and public benchmarks, the most capable models have often remained proprietary, creating risks of concentration among a small number of companies. It contends that increasingly capable open-weight models can broaden competition, but their practical success depends on production infrastructure such as efficient inference, low latency, reliability, and affordability. Nebius says it supports customer choice between open and closed models while contributing to the open ecosystem through inference optimizations, research such as LK losses for speculative decoding, open-source tools including Kvax and Soperator, hardware designs, benchmarks, agent-development resources, and research funding. The company maintains that openness does not inherently ensure quality or safety, but enables more people to inspect, test, reproduce, improve, and address failures in AI systems. It frames open and proprietary AI as complementary rather than mutually exclusive, arguing that a durable open ecosystem is necessary to preserve choice, scientific scrutiny, and the broad circulation of useful ideas across the AI stack.

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