‎AI-focused crypto tokens are increasingly facing a credibility gap as the market moves past broad narratives and into closer scrutiny.

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‎ Early enthusiasm was driven by ambitious ideas such as decentralized intelligence, open-access models, and global coordination through tokenized networks. While these concepts captured attention, they often lacked concrete benchmarks.
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‎As the sector matures, investors and traders are now asking more practical questions: How much throughput do these systems handle today? What is the quality and exclusivity of the data they rely on? Is there measurable demand that exists independently of token price speculation?
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‎As these questions surface, valuation frameworks begin to shift. Tokens without clear indicators of real usage or sustainable demand are being repriced, sometimes rapidly. This repricing does not necessarily imply that the underlying technology has failed, but it does reflect a growing distinction between long-term potential and short-term market expectations. In practice, this leads to sharper rotations rather than gradual adjustments, particularly around AI-related assets where timelines and outcomes remain uncertain.
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‎Within the $TON ecosystem, moments like these highlight the importance of execution infrastructure. When AI tokens such as $FET experience sentiment-driven repricing, capital needs to move efficiently. STONfi operates in this context as a background layer rather than a narrative driver. Its role is not to validate AI theses, but to facilitate fast, predictable exchanges as traders adjust exposure during periods of reassessment.
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‎As hype cycles fade, market structure becomes more influential than storytelling. Attention may open the door, but survival increasingly depends on demonstrable proof of usage and the ability for markets to reprice expectations smoothly.

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