Frontier models are currently developed and distributed primarily through two channels: centralized proprietary APIs or open-sourcing of pre-trained weights. We identify a third paradigm - Protocol Learning - where models are trained across decentralized networks of incentivized participants. This approach has the potential to aggregate orders of magnitude more computational resources than any single centralized entity, enabling unprecedented model scales and capabilities. However, it also introduces novel challenges: heterogeneous and unreliable nodes, malicious participants, the need for unextractable models to preserve incentives, and complex governance dynamics. To date, no systematic analysis has been conducted to assess the feasibility of Protocol Learning or the associated risks, particularly the 'No-Off Problem' arising from the inability to unilaterally halt a collectively trained model. We survey recent technical advances that suggest decentralized training may be feasible - covering emerging communication-efficient strategies and fault-tolerant methods - while highlighting critical open problems that remain. Contrary to the notion that decentralization inherently amplifies frontier risks, we argue that Protocol Learning's transparency, distributed governance, and democratized access ultimately reduce these risks compared to today's centralized regimes.