  Blockchains rely on a consensus among participants to achieve
decentralization and security. However, reaching consensus in an online,
digital world where identities are not tied to physical users is a challenging
problem. Proof-of-work provides a solution by linking representation to a
valuable, physical resource. While this has worked well, it uses a tremendous
amount of specialized hardware and energy, with no utility beyond blockchain
security. Here, we propose an alternative consensus scheme that directs the
computational resources to the optimization of machine learning (ML) models, a
task with more general utility. This is achieved by a hybrid consensus scheme
relying on three parties: data providers, miners, and a committee. The data
provider makes data available and provides payment in return for the best
model, miners compete about the payment and access to the committee by
producing ML optimized models, and the committee controls the ML competition.
