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first_img Hashed Anchored 300 Million Dollar Digital Asset Private Credit Fund

The cryptocurrency venture capital firm Hashed has anchored a new digital asset private credit fund with a target size of $300 million. The fund was founded by Abu Dhabi investor and Further Ventures co-founder Mohamed Hamdy, and is managed by Thoro Capital Management, where he serves as managing partner.Thoro will lend directly to digital asset institutions in US dollars settled through stablecoins, with Hashed acting as the primary investor in the fund. Hashed stated that the new fund aims to address key financing bottlenecks in the institutional digital asset space—traditional banks are constrained by regulatory capital requirements, while existing crypto lenders underwrite based on asset collateral, which forces even profitable and audited market infrastructure companies to rely on expensive, short-term secured borrowing.The fund adopts a "contract-based" underwriting approach, assessing borrowers' financial conditions, cash flows, and management performance. Hashed noted that tokenized private credit has become the largest real-world asset (RWA) category by cumulative on-chain lending, with total loans exceeding $14 billion, while the traditional private credit market exceeds $30 trillion. Previously, Hashed obtained a financial services license issued by the Abu Dhabi Global Market and signed a memorandum of understanding with the Abu Dhabi Investment Office last week.

first_img Viewpoint: The AI application layer should not be priced based on tokens, but should be anchored to "recognizable work value."

a16z partner Sarah Wang recently published an article pointing out that AI application layer products should not price based on tokens like the model layer, but rather on "recognizable work units." The article argues that token pricing anchors the value of application products to a unit whose cost is continuously declining, making it difficult for customers to predict context length, retrieval volume, or reasoning time, and improperly compares applications to raw computing power.The article suggests a tiered pricing model based on value levels: model layer priced by tokens; application layer priced by recognizable work units for customers (such as account research briefs, code modifications, completed queries), which can be encapsulated through Credits; and scenarios that are attributable and have clear value priced directly by results (such as resolved customer service conversations, qualified leads). The design of Credits should map to different levels of work difficulty to protect gross margins and distinguish "work value" from "delivery cost." The article uses Clay as an example, where its new pricing separates Data Credits (third-party data) from Actions (orchestrated work), only passing on costs for reasoning models with significant cost fluctuations without markup. The author believes that pricing anchored to value rather than computing cost allows customers to understand spending in relation to value, while also benefiting product providers in maintaining profit margins.
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