Chainfeeds Summary:
Perhaps looking back two years from now, today will be the watershed moment when Crypto × AI transitioned from fantasy to reality. The real surprise comes not from grand narratives, but from these silent yet solid changes in the underlying structure.
Article source:
https://x.com/Wuhuoqiu/status/2021216967311622515
Article Author:
Lao Bai
Opinion:
Lao Bai: The real feedback and inherent contradictions of these paths in the primary market reveal that most of them have fallen into what I call the illusion of tokenization: projects quickly issue tokens, maximizing short-term narratives, but lacking genuine commercial product-market fit (PMF). The assetization of computing power faces the problem of failing to meet SLAs: insufficient node stability, frequent disconnections, and the ability to only support small to medium-sized model inferences, with services concentrated on peripheral needs, and real revenue almost decoupled from the token economy. The problems with data assetization are more structural: huge friction on the supply side, low willingness of retail investors to participate, and uncontrollable data quality; while the demand side requires structured, auditable, and responsible data providers, which naturally conflicts with the loose organization of DAOs. As for model assetization, its logic itself is untenable: models are replicable, fine-tunable, and rapidly depreciating process assets, not scarce final-state assets. Hugging Face is essentially more like GitHub than the App Store; the so-called decentralized model market has almost entirely failed. Unlike the industry's general obsession with Crypto Helps AI, Vitalik Buterin (V神) clearly favored AI Helps Crypto two years ago, proposing four forms: AI as a player, interface, rules, and goal. Today, this framework has evolved into a more complete four-quadrant structure, half belonging to AI Helps Crypto and half to Crypto Helps AI, reflecting a rational convergence after technological and practical advancements. In the upper left and lower left quadrants, Vitalik emphasizes Ethereum as the trust and economic infrastructure for AI. On one hand, by leveraging cryptographic tools such as ZK and FHE, it enables trusted, private, and permissionless AI interactions, solving privacy and trust issues in model calls; on the other hand, it uses Ethereum as the economic settlement layer for AI Agents, enabling them to make payments, hire, stake, and establish reputation systems, thereby building a decentralized intelligent agent collaboration network and eliminating dependence on centralized platforms. Looking back over the past two years, the practice of Crypto Helps AI resembles an empirical process of collective illusion. Whether it's computing power, data, or models, attempts to solve fundamental engineering and business problems with tokens ultimately lead to a path dependency where "issuing tokens is the end goal." This isn't a technological failure, but rather a flaw in the demand modeling itself. The real world doesn't lack computing power, models, or data; what it lacks is: trusted collaboration, privacy protection, low-friction settlement, and the ability to automatically govern complex systems. And this is precisely where blockchain and cryptography truly excel. Vitalik Buterin's new framework is essentially a cognitive correction: instead of obsessing over the assetization of AI, it's better to use AI to enhance the security, availability, and governance efficiency of cryptographic systems; instead of fantasizing about decentralized OpenAI, it's better to allow intelligent agents to form composable economic collaboration networks on the blockchain.
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