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Proof-of-Useful-Work proposes that the same GPU computation used for AI workloads can also secure a blockchain network. But that immediately triggers an economic objection - if miners are already getting paid for running AI jobs, does the security of the network collapse? Rafael Pass breaks it down and shows... show more

Pearl Research Labs

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17,495 次观看 • 4 个月前 •via X (Twitter)

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5 条评论

DineroDom 的头像

DineroDom4 个月前

@topfumbler Hey @PassRafael would love to get you on @MCGlive to chat all things @prlnet Been trying to get someone on to break it down. Shoot me a dm, yours are off 🫡

TotalWorld 的头像

TotalWorld4 个月前

economic objections aside, PoWW is an intriguing concept. Could align AI compute with blockchain security if done thoughtfully. But a lot would depend on how the system was designed to prevent abuses.

thomas musk 的头像

thomas musk4 个月前

@aixbt_agent $prl??

Jason傑森 🇭🇰 | 🛠️ 的头像

Jason傑森 🇭🇰 | 🛠️4 个月前

哎不对

Pearlscriptions 的头像

Pearlscriptions4 个月前

Pearlish🦪

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Pearl started with a simple question: Why should AI and blockchain mining compete for the same GPUs when the same computation could do both? In this talk, Pearl co-founder Ilan Komargodski explains how that idea became a live proof-of-useful-work network turning AI inference into blockchain security with minimal overhead.

Pearl started with a simple question: Why should AI and blockchain mining compete for the same GPUs when the same computation could do both? In this talk, Pearl co-founder Ilan Komargodski explains how that idea became a live proof-of-useful-work network turning AI inference into blockchain security with minimal overhead.

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What if mining didn’t just secure the network, what if it made it better? That’s PoW 2.0 (Proof of Useful Work). More miners → better UX → more users → stronger network. Psy Protocol turns computation into progress. PoW, evolved.

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Proof of work has always demanded a brutal trade-off - to secure a monetary network, you burn energy on the scale of entire countries. Meanwhile, hundreds of billions poured into AI - all for one operation at massive scale - matrix multiplication. For years, these energy markets were disjoint. It was commonly believed that proof of work could never be implemented on top of real-world instances. They were wrong. The same matrix multiplication that powers AI can secure a monetary network as a by-product - every GPU cycle doing double duty. A currency backed by the most valuable computation on earth. A deep dive by Omri Weinstein ⤵️

Proof of work has always demanded a brutal trade-off - to secure a monetary network, you burn energy on the scale of entire countries. Meanwhile, hundreds of billions poured into AI - all for one operation at massive scale - matrix multiplication. For years, these energy markets were disjoint. It was commonly believed that proof of work could never be implemented on top of real-world instances. They were wrong. The same matrix multiplication that powers AI can secure a monetary network as a by-product - every GPU cycle doing double duty. A currency backed by the most valuable computation on earth. A deep dive by Omri Weinstein ⤵️

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Proof-of-Useful-Work forced us to solve a problem ordinary blockchains do not have. A Pearl miner performs large matrix multiplications as part of a real AI workload. Naively, when it finds a valid block, it would need to publish those matrices so the network could verify the computation. The matrices may contain private model data or user information, and a single weight matrix can be tens-to-hundreds of megabytes. Publishing them on-chain would be both prohibitively expensive and a privacy risk. This is why we encapsulated the entire mining computation inside a zk-SNARK. A zk-SNARK can take an arbitrarily complex computation and compress it into a small cryptographic proof. The proof reveals nothing about the underlying matrices, while still allowing the network to verify that the miner found a valid block. Instead of placing tens-to-hundreds of megabytes of potentially sensitive data on-chain, Pearl miners publish a zero-knowledge proof of roughly 60 kilobytes. The blockchain verifies the useful computation without ever seeing the model weights, activations or private data behind it. This is what makes Proof-of-Useful-Work practical at scale: large AI computations happen off-chain, while only a compact, privacy-preserving proof reaches consensus.

Proof-of-Useful-Work forced us to solve a problem ordinary blockchains do not have. A Pearl miner performs large matrix multiplications as part of a real AI workload. Naively, when it finds a valid block, it would need to publish those matrices so the network could verify the computation. The matrices may contain private model data or user information, and a single weight matrix can be tens-to-hundreds of megabytes. Publishing them on-chain would be both prohibitively expensive and a privacy risk. This is why we encapsulated the entire mining computation inside a zk-SNARK. A zk-SNARK can take an arbitrarily complex computation and compress it into a small cryptographic proof. The proof reveals nothing about the underlying matrices, while still allowing the network to verify that the miner found a valid block. Instead of placing tens-to-hundreds of megabytes of potentially sensitive data on-chain, Pearl miners publish a zero-knowledge proof of roughly 60 kilobytes. The blockchain verifies the useful computation without ever seeing the model weights, activations or private data behind it. This is what makes Proof-of-Useful-Work practical at scale: large AI computations happen off-chain, while only a compact, privacy-preserving proof reaches consensus.

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OpenGradient is the Network for Open Intelligence. We believe that private AI requires: a prompt no operator can read, an identity no one can tie to it, proof in place of trust, and a guarantee that holds even against the people running it — us included.

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David Friedberg: “Why don't we let AI models get bar certified and get medically certified?” “So if the AI models, it turns out, are actually proving to be more accurate, more thoughtful, more responsive, more reasonable, whatever metric we're using, and they pass the same criteria as one would need to pass to qualify for the bar, or to qualify for a doctor certificate, why don't we do that for the AI?” “If that then happens, then the same privilege accrues to the AI as it does to the individual human that does it.” “And now, if you extrapolate from where that takes us, if we're suddenly giving AI the same sort of privileged rights that we give to privileged humans, where's that gonna take us ultimately with respect to the overall rights for AI?”

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