# AI-Based Crypto Tokens: The Illusion of Decentralized AI?

arXiv: https://arxiv.org/abs/2505.07828 · HTML: https://mlsanity.com/paper/2505.07828/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 97 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 18 of 18)

- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Red-Teaming for Generative AI: Silver Bullet or Security Theater?](https://mlsanity.com/paper/2401.15897/) (2024)
- [Fusing Blockchain and AI with Metaverse: A Survey](https://mlsanity.com/paper/2201.03201/) (2022)
- [BitTensor: A Peer-to-Peer Intelligence Market](https://mlsanity.com/paper/2003.03917/) (2020)
- [Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning](https://mlsanity.com/paper/1903.09800/) (2019)
- [A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Data](https://mlsanity.com/paper/2112.02870/) (2021)
- [Proof of Quality: A Costless Paradigm for Trustless Generative AI Model Inference on Blockchains](https://mlsanity.com/paper/2405.17934/) (2024)
- [IPFS and Friends: A Qualitative Comparison of Next Generation Peer-to-Peer Data Networks](https://mlsanity.com/paper/2102.12737/) (2021)
- [A Flash(bot) in the Pan: Measuring Maximal Extractable Value in Private Pools](https://mlsanity.com/paper/2206.04185/) (2022)
- [A Proof of Useful Work for Artificial Intelligence on the Blockchain](https://mlsanity.com/paper/2001.09244/) (2020)
- [Layer 2 Blockchain Scaling: a Survey](https://mlsanity.com/paper/2107.10881/) (2021)
- [Advancing Blockchain Scalability: An Introduction to Layer 1 and Layer 2 Solutions](https://mlsanity.com/paper/2406.13855/) (2024)
- [Should Artificial Intelligence Governance be Centralised? Design Lessons from History](https://mlsanity.com/paper/2001.03573/) (2020)
- [Blockchain and Artificial Intelligence](https://mlsanity.com/paper/1802.04451/) (2018)
- [User Incentives for Blockchain-based Data Sharing Platforms](https://mlsanity.com/paper/2110.11348/) (2021)
- [Smart Contract Languages: a comparative analysis](https://mlsanity.com/paper/2404.04129/) (2024)
- [Bitcoin vs. Bitcoin Cash: Coexistence or Downfall of Bitcoin Cash?](https://mlsanity.com/paper/1902.11064/) (2019)
- [Data Availability and Decentralization: New Techniques for zk-Rollups in Layer 2 Blockchain Networks](https://mlsanity.com/paper/2403.10828/) (2024)

## Built on by (most cited first, 2 of 2)

- [On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation](https://mlsanity.com/paper/2510.16024/) (2025)
- [Fortytwo: Swarm Inference with Peer-Ranked Consensus](https://mlsanity.com/paper/2510.24801/) (2025)

## Abstract (arXiv)

The convergence of blockchain and artificial intelligence (AI) has led to the emergence of AI-based tokens, which are cryptographic assets designed to power decentralized AI platforms and services. This paper provides a comprehensive review of leading AI-token projects, examining their technical architectures, token utilities, consensus mechanisms, and underlying business models. We explore how these tokens operate across various blockchain ecosystems and assess the extent to which they offer value beyond traditional centralized AI services. Based on this assessment, our analysis identifies several core limitations. From a technical perspective, many platforms depend extensively on off-chain computation, exhibit limited capabilities for on-chain intelligence, and encounter significant scalability challenges. From a business perspective, many models appear to replicate centralized AI service structures, simply adding token-based payment and governance layers without delivering truly novel value. In light of these challenges, we also examine emerging developments that may shape the next phase of decentralized AI systems. These include approaches for on-chain verification of AI outputs, blockchain-enabled federated learning, and more robust incentive frameworks. Collectively, while emerging innovations offer pathways to strengthen decentralized AI ecosystems, significant gaps remain between the promises and the realities of current AI-token implementations. Our findings contribute to a growing body of research at the intersection of AI and blockchain, highlighting the need for critical evaluation and more grounded approaches as the field continues to evolve.