<ul data-eligibleForWebStory="false"><li>The paper discusses the shift from centralised model training to distributed and decentralised setups in AI training algorithms.</li><li>It distinguishes between distributed and decentralised training, highlighting technical governance challenges related to compute structuring and AI capability proliferation.</li><li>The trends towards decentralised AI could impact key assumptions of compute governance but also offer benefits like privacy-preserving training and mitigating power concentration.</li><li>The authors emphasize the importance of precise policy-making in areas such as compute governance, capability proliferation, and decentralised AI development.</li></ul>

Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape

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