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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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13,540 görüntüleme • 2 ay önce •via X (Twitter)

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@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 🫡

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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.

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@aixbt_agent $prl??

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哎不对

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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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"I used to be a bitcoiner. The transition to a new store of value only happens once every 3,000 years. That's the main prize -- just focus on that. But [security] is the criteria that ultimately convinced me to flip from Bitcoin to ETH." "I have a higher degree of certainty that Ethereum will be around longer [than Bitcoin]. The reason for that is because Bitcoin relies on proof-of-work, which is less efficient than proof-of-stake and doesn't scale with the value of the network. And as the block subsidy of Bitcoin halves every four years, it is increasingly becoming more and more reliant on transaction fees to fund the security budget paid to miners." "If you look at [Bitcoin's] security budget right now, about 0.6% of revenue to miners is transaction fees... The problem with that is if Bitcoin becomes 'digital gold', flips gold, and becomes a $30 trillion asset, but it only costs $10-20 billion to attack it, that's too asymmetric." "You want the security budget to scale with the market cap, similar to how countries spend a % of their GDP on defense. The more valuable something is, the more you need to spend to protect it." "Ethereum, with the Merge, migrated to proof-of-stake, which is fundamentally more secure because it's less reliant on transaction fees and it scales with the value of the network. If 1/3rd of ETH is staked and then you need 1/3rd of those ETH to censor the network, you're looking at roughly 10% of the total market cap as the cost to attack the network." "So if Ethereum flips Bitcoin and gold and becomes a $30 trillion asset, it'll cost ~$3 trillion to attack the Ethereum network versus Bitcoin at like $10 billion." "The other aspect here is that as AI hyperscalers invest more and more in AI, proof-of-work becomes increasingly vulnerable because the cost to attack the Bitcoin network is starting to look close to the quarterly CapEx these hyperscalers are spending on their data centers." Full interview on Bankless with Vivek Raman discussing the new Etherealize "Productive Money" report below.

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“What if the real unlock for AI x crypto is not speculation — but proving intelligence can be trusted?” Sachi Takamura / 0699.eth sits down with dcbuilder.eth ⚪️ for a deep dive into the intersection of AI and crypto, from DC’s work building the ZKML community to why zero-knowledge machine learning could become a core trust layer for AI on the internet. They break down how ZKML makes it possible to prove that a machine learning model produced a specific output from specific inputs — without forcing everyone to rerun the full computation themselves. The conversation also explores why this matters for model accountability, transparency, and verifiable AI at scale, plus the projects pushing the space forward, from Modulus Labs and Giza to EZKL and new research around proof of inference.

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“What if the real unlock for AI x crypto is not speculation — but proving intelligence can be trusted?” Sachi Miyasaki sits down with dcbuilder.eth ⚪️ for a deep dive into the intersection of AI and crypto, from DC’s work building the ZKML community to why zero-knowledge machine learning could become a core trust layer for AI on the internet. They break down how ZKML makes it possible to prove that a machine learning model produced a specific output from specific inputs — without forcing everyone to rerun the full computation themselves. The conversation also explores why this matters for model accountability, transparency, and verifiable AI at scale, plus the projects pushing the space forward, from Modulus Labs and Giza to EZKL and new research around proof of inference.

“What if the real unlock for AI x crypto is not speculation — but proving intelligence can be trusted?” Sachi Miyasaki sits down with dcbuilder.eth ⚪️ for a deep dive into the intersection of AI and crypto, from DC’s work building the ZKML community to why zero-knowledge machine learning could become a core trust layer for AI on the internet. They break down how ZKML makes it possible to prove that a machine learning model produced a specific output from specific inputs — without forcing everyone to rerun the full computation themselves. The conversation also explores why this matters for model accountability, transparency, and verifiable AI at scale, plus the projects pushing the space forward, from Modulus Labs and Giza to EZKL and new research around proof of inference.

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Mo Gawdat believes up to 30% of jobs in certain sectors could disappear by 2028. That stopped me in my tracks. Mo was one of the first people to come on this podcast and warn me about AI, long before most of the world was talking about it. At the time, it felt early. Now, it feels like the world is catching up to what he was seeing. I’m still trying to understand what AI actually means for our lives. Not just whether it can write emails, create images or make us more productive. I mean what it does to jobs. What it does to power. What it does to education. What it does to human connection… That’s why I wanted to have this discussion with Mo again. What makes Mo worth listening to is that he saw these systems inside Google years before most of us had even heard the term AI. His book Scary Smart now feels like it was written for this exact moment. Let me explain why this discussion matters. Mo believes we’re not just entering an AI revolution. We’re entering a period where AI, robotics, economics, surveillance, digital currencies and global instability are all colliding at the same time. That’s a lot for any of us to process. We spoke about: - The jobs Mo believes are most at risk from AI. - Why he believes that AI is actually underhyped! - The mistake almost everyone is making with ChatGPT. - The prediction that changed even his own view of the future. The part that stayed with me was this idea that human connection may become the real currency. Because if AI can produce the information, write the report, analyse the data, then what is left? I don’t think this conversation gives neat answers. That’s probably why it’s worth watching. It helped me think more honestly about what’s coming.

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Honor is building the first phone that also includes an AI robot. It's a robot in the sense that the pop-up camera acts as the AI's eyes, and if I understand correctly, it allows a continuously active AI companion to work as an assistant. Interesting, but probably more of a gimmick. However, personal AI companions are coming.

Honor is building the first phone that also includes an AI robot. It's a robot in the sense that the pop-up camera acts as the AI's eyes, and if I understand correctly, it allows a continuously active AI companion to work as an assistant. Interesting, but probably more of a gimmick. However, personal AI companions are coming.

Chubby♨️

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Anthropic co-founder Ben Mann has the clearest definition of AGI I've heard, which he calls The Economic Turing Test: "If you contract an agent for 1-3 months on a particular job and it turns out to be a machine rather than a person, then it's passed the Economic Turing Test for that role. And if the agent can pass that test for 50% of money-weighted jobs, then we have transformative AI. If we pass that threshold, then we would expect massive effects on world GDP increases, and societal change, and the number of people that are employed once these things are possible. That's the start of a new era."

Anthropic co-founder Ben Mann has the clearest definition of AGI I've heard, which he calls The Economic Turing Test: "If you contract an agent for 1-3 months on a particular job and it turns out to be a machine rather than a person, then it's passed the Economic Turing Test for that role. And if the agent can pass that test for 50% of money-weighted jobs, then we have transformative AI. If we pass that threshold, then we would expect massive effects on world GDP increases, and societal change, and the number of people that are employed once these things are possible. That's the start of a new era."

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