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AI Reference 096 Robotics, Finance & Governance Wikipedia guided Primary sources linked

Asset Tokenization

Definition

Asset tokenization represents claims on financial or physical assets as digital tokens on programmable platforms. BIS work treats tokenization as a change in representation, transfer and settlement infrastructure—not as a form of artificial intelligence.

What is asset tokenization?

Asset tokenization is the representation of rights or claims on an asset as digital tokens on a programmable ledger or blockchain. The underlying asset can be financial or physical, including securities, funds, real estate or commodities.

What has to exist behind the token

A token does not magically create legal ownership. A real system needs a legal link between the token and the underlying asset, custody arrangements, rules for transfer and often identity and compliance checks.

Why finance is interested

BIS work highlights possible improvements in transfer, settlement and programmability while also warning about liquidity, operational, legal and financial-stability risks. Tokenization is a financial-market infrastructure concept, not a type of AI.

Related terms, defined

Robotics, Finance & Governance AI and Tokenized Financial Assets AI and tokenization are distinct technologies that can interact. AI can support monitoring, compliance, analysis or operational automation around tokenized markets; tokenization itself is a ledger and market-structure technology. Robotics, Finance & Governance The NIST AI Risk Management Framework The NIST AI Risk Management Framework is a voluntary framework organized around governing, mapping, measuring and managing AI risk. NIST's Generative AI Profile extends that framework with risks and suggested actions specific to generative systems. Robotics, Finance & Governance AI Governance and Regulation AI governance is the set of policies, ownership, controls, documentation, evaluation and monitoring used to manage AI systems; regulation is one external component of that broader governance system. Robotics, Finance & Governance AI Roadmap 2026: Agents, Multimodality, Efficiency and Robotics As of 2026, first-party releases and independent measurement show several major directions: more tool-using and agentic systems, stronger multimodality, smaller efficient models, longer context, model routing, open-weight competition and closer integration between foundation models and robotics. Robotics, Finance & Governance AI and Robotics AI and robotics combine perception, planning, control and learning with physical machines. Unlike a text-only application, a robot must operate under sensor noise, timing constraints, changing environments and physical safety requirements.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.