# Verifiable Inference Articoli collegati

Il Centro Notizie HTX fornisce gli articoli più recenti e le analisi più approfondite su "Verifiable Inference", coprendo tendenze di mercato, aggiornamenti sui progetti, sviluppi tecnologici e politiche normative nel settore crypto.

Tokens, Models, and Bubbles: The Crypto × AI Game in the Primary Market

Based on a two-year retrospective, this article analyzes the convergence of Crypto and AI from a primary market perspective. Initially, the crypto space heavily promoted "Crypto Helps AI" through three main narratives: computation power tokenization, data tokenization, and model tokenization. However, these efforts largely resulted in what the author calls a "tokenization illusion"—projects that issued tokens but lacked real product-market fit or sustainable business models. The piece critiques these approaches: decentralized compute networks often fail to meet enterprise reliability standards; tokenized data struggles with supply-demand alignment due to low user motivation and high professional requirements; and model tokenization is fundamentally flawed since AI models are non-scarce, easily replicable, and depreciate quickly. Additionally, projects focusing on verifiable inference (like ZKML or OPML) are solutions in search of a problem, as real-world AI failures are rarely due to malicious tampering but rather design errors or misconfigurations. The author references Vitalik Buterin’s updated views, which now present a more balanced perspective compared to two years ago. Buterin outlines four quadrants of Crypto × AI integration: two where crypto (especially Ethereum) provides trustless, economic layers for AI agents and private interactions, and two where AI enhances crypto—through local LLMs acting as user shields for security and AI improving market efficiency and DAO governance. The conclusion emphasizes that meaningful progress lies at the intersection of both fields, beyond mere tokenization or speculative narratives, and expresses hope for more substantive developments in the future.

比推02/12 06:16

Tokens, Models, and Bubbles: The Crypto × AI Game in the Primary Market

比推02/12 06:16

Viewing Crypto × AI from the Primary Market: An Experiment in Tokenization Illusion

This article analyzes the convergence of cryptocurrency and artificial intelligence (AI), critiquing the prevailing trend of "tokenization illusion" in Crypto × AI projects. Initially, the discussion highlights Vitalik Buterin's earlier skepticism toward Crypto-Helps-AI initiatives—such as tokenizing computing power, data, and AI models—which often lack sustainable product-market fit. These efforts face issues like unreliable compute services, low-quality data supply, and the non-scarce nature of AI models. The author also questions the practicality of verifiable inference (e.g., ZKML), arguing it addresses non-existent threats, as AI errors typically stem from design flaws rather than malicious tampering. Buterin’s updated perspective now balances both AI-Helps-Crypto and Crypto-Helps-AI, focusing on four key areas: using Ethereum for trustless AI interactions and economic layers for AI agents, and leveraging AI for smart contract auditing, prediction markets, and DAO governance. The conclusion emphasizes a more mature, integrated approach beyond mere tokenization, hoping for tangible progress in the coming years.

marsbit02/12 06:11

Viewing Crypto × AI from the Primary Market: An Experiment in Tokenization Illusion

marsbit02/12 06:11

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