At the heart of Avalon’s concept lies a provocative idea: AI models can exist as on-chain resources, tradable and accessible like any digital asset. The company’s newly released whitepaper introduces an ecosystem that turns pre-trained machine learning models into tokenized services available on a decentralized marketplace.
Instead of renting GPU capacity – the norm in today’s AI economy – developers and institutions will be able to purchase or lease access to models already trained and optimized for specific use cases. Avalon calls this mechanism AI-Model-as-a-Service (AI-MaaS), a structure it claims will democratize access to powerful AI without the need for technical overhead or infrastructure costs.
The inaugural models, built on H200 GPU hardware, are designed to deliver enterprise-grade performance that can be integrated directly into smart contracts, dApps, or research pipelines.
Avalon’s second major innovation isn’t about computation – it’s about law. The company has created a framework called Commercial Rights Tokenization (CRT), which changes the way real-world assets are represented on-chain.
Rather than tokenizing ownership stakes, Avalon focuses on rights – the legal permissions to use, rent, or access a service. Each token represents a claim recognized under U.S. commercial statutes, specifically the Uniform Commercial Code’s Articles 7, 9, and 12.
That means token holders can hold enforceable digital rights with legal weight, bridging the gap between smart-contract execution and real-world enforceability. This model may ultimately provide a compliance-friendly path for institutions to participate in blockchain markets.
Avalon’s marketplace uses a two-tier financial structure resembling traditional debt markets:
Both allocations represent rental rights to AI model usage rather than speculative tokens. The dual-tier format offers a blend of stability and dynamism – effectively turning compute access into a tradable, yield-generating digital right.
The whitepaper outlines a comprehensive risk management and liquidity framework, including automated monitoring, redemption scheduling, and transparent reporting – all on-chain. These guardrails are meant to ensure that even as AI and finance converge, accountability remains built into the system.
If successful, Avalon’s approach could establish a new class of decentralized financial infrastructure, where AI models, legal rights, and digital tokens co-exist within the same economic layer.
By merging AI utility, blockchain transparency, and legal enforceability, Avalon Labs is building more than a marketplace – it’s constructing a blueprint for what the next decade of digital economies could look like.
Its premise is both radical and pragmatic: that one day, artificial intelligence won’t just power financial systems – it will become part of them.
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