#去中心化金融应用 Giza's AI Agent asset management scale has surpassed $40 million, with a monthly increase of over 60%, and this data is worth paying attention to. The core logic is to ensure AI decision transparency through zero-knowledge proofs, allowing the Agent to autonomously execute trading strategies—this addresses the trust issue in DeFi automation management.
From an on-chain perspective, several key signals: first, the speed of capital inflow, with a 60% monthly increase indicating growing market recognition of verifiable AI; second, the breakthrough point in scale, $40 million may not be a large proportion of the total DeFi market, but the rapid growth reflects increasing attractiveness of this infrastructure; third, application scenarios, such as market analysis, asset optimization, and automated execution of trading strategies, essentially aim to reduce manual operation costs and slippage risks.
It should be noted that the sustainability of such products depends on actual trading performance. High growth rates are common in the early stages; the key is whether they can maintain stable returns afterward. It is recommended to track their capital outflows and actual AUM fluctuations—if large amounts of capital are frequently entering and leaving, it indicates the market is still observing; if the accumulated funds increase, it suggests this model has been validated.
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#去中心化金融应用 Giza's AI Agent asset management scale has surpassed $40 million, with a monthly increase of over 60%, and this data is worth paying attention to. The core logic is to ensure AI decision transparency through zero-knowledge proofs, allowing the Agent to autonomously execute trading strategies—this addresses the trust issue in DeFi automation management.
From an on-chain perspective, several key signals: first, the speed of capital inflow, with a 60% monthly increase indicating growing market recognition of verifiable AI; second, the breakthrough point in scale, $40 million may not be a large proportion of the total DeFi market, but the rapid growth reflects increasing attractiveness of this infrastructure; third, application scenarios, such as market analysis, asset optimization, and automated execution of trading strategies, essentially aim to reduce manual operation costs and slippage risks.
It should be noted that the sustainability of such products depends on actual trading performance. High growth rates are common in the early stages; the key is whether they can maintain stable returns afterward. It is recommended to track their capital outflows and actual AUM fluctuations—if large amounts of capital are frequently entering and leaving, it indicates the market is still observing; if the accumulated funds increase, it suggests this model has been validated.