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The spot Bitwise NEAR ETF (NRR) made a strong debut on NYSE Arca. The company's chief investment officer, Matt Hougan, officially confirmed its first major milestone: after just two trading days, the fund's assets under management (AUM) surpassed $52.8 million, rising sharply from $36 million on its debut day.
Commenting on the initial results, Bitwise's CIO described the debut as a "strong launch." On its second day, the fund recorded net inflows of $13.2 million, while total trading volume reached $20.1 million.
For an annual fee of 0.75%, the fund offers Wall Street direct exposure to the NEAR token with built-in staking, which generates an annual yield of around 5%, automatically increasing the net asset value (NAV) of investors' holdings.
What drove the capital inflows on the second trading day
Excitement around the listing was fueled by an accompanying 40-page report in which Matt Hougan and head of European research André Dragosch proposed valuing NEAR Protocol as a global settlement hub for autonomous AI agents. Applying a conservative SaaS valuation methodology with a 50x multiple to blockchain fees, the authors outlined two scenarios for the asset's future:
- Bull case ($562.81 by 2030): The network captures 3% of the global AI agent market (around 950 million programs), serving a role similar to Visa's in traditional payments.
- Bear case ($1.63): Centralized giants such as OpenAI retain a monopoly on the AI market, leaving decentralized IT infrastructure without a practical use case.
The fund's launch coincided with a strong local uptrend in NEAR. In September, the token's price rose parabolically and nearly tripled from its August lows, settling around $4.99. Fundamental metrics also support the asset: the network firmly holds second place globally by the number of active addresses (47 million), driven by the development of fast cross-chain transactions through NEAR Intents.
Alongside Bitwise's existing crypto funds, the new ETF successfully filled an open premium niche at the intersection of Web3 and generative artificial intelligence, meeting management's expectations for its initial performance.
Arman Shirinyan
Dan Burgin
