Techub, citing a post from @Delphi_Digital, said Proof-of-Useful-Work blockchain Pearl is designed to let miners use the same compute resources for AI inference and block production. The project aims to create two complementary revenue streams: block rewards would help subsidize inference costs, while payments from customers would help cover the base cost of mining. Pearl uses matrix multiplication as its mining primitive, the same operation that sits at the core of AI inference. When miners handle compatible inference requests, the same computation can both generate mining attempts and produce outputs for customers. Miners can also run arbitrary matrix multiplications solely to compete for block rewards. The protocol checks whether the required computation was executed correctly, rather than whether a matrix came from a real inference request. Under that setup, miners serving real inference demand may be able to earn both customer fees and mining rewards from the same underlying work, giving them a stronger economic model than miners running only synthetic computation.
Techub reported, citing a post from @Delphi_Digital, that Proof-of-Useful-Work blockchain Pearl is built around the idea of letting miners use the same compute resources to run AI inference while also competing to produce blocks.
The stated goal is to create two complementary revenue streams. Block rewards would subsidize inference costs, while customer payments would cover the base cost of mining.
Matrix multiplication sits at the center of Pearl’s design
Pearl uses matrix multiplication as its mining primitive. That is also a core operation in AI inference. Under this setup, when miners process compatible inference requests, the same computation can generate mining attempts and produce outputs for customers at the same time.
Miners are not limited to customer-driven workloads. They can also perform arbitrary matrix multiplications purely to compete for block rewards.
The protocol verifies execution, not request origin
The protocol is designed to verify whether the prescribed computation was carried out correctly. It does not verify whether the matrix came from a real inference request.
In economic terms, miners that execute real inference jobs can earn customer fees and mining rewards from the same underlying work. Compared with miners that run only synthetic computation, that model may offer a stronger economic advantage.
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