The recent rise of Bitcoin has sparked an unprecedented trend of enthusiasts acquiring expensive hardware for mining. This self-perpetuating race, driven by Bitcoin’s PoW consensus mechanism, has led to the creation of massive computational centers dedicated solely to solving impractical hash inversions. However, these computational resources could be redirected toward more meaningful tasks if nodes were properly incentivized. In this paper, we introduce Proof of Training (PoT), a novel consensus mechanism that offers two key advantages over previous approaches. First, it replaces wasteful computations with ML training. Second, by aligning the inherent distrust between nodes in blockchain networks with distributed model training, PoT not only achieves consensus but also produces a trained model along with proof that it was trained on the client-provided data. PoT enables clients to hire the blockchain network to provably train arbitrary models using their provided datasets and architectures. Meanwhile, nodes that participate in training are rewarded with PoT cryptocurrency based on their computational contributions.