Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Blockchain and Artificial Intelligence: Synergies and Conflicts

Leon Witt, Armando Teles Fortes, Kentaroh Toyoda, Wojciech Samek, Dan Li

TL;DR

The paper investigates how blockchain and AI can complement or conflict with each other, highlighting both theoretical synergies and real-world obstacles. It uses a bottom-up analysis of Blockchain X AI projects with market capitalization over USD 10 million to develop a four-cluster taxonomy: AI peripheral to blockchain, AI participating in blockchain, blockchain managing AI processes, and blockchain as the core infrastructure for AI. It discusses dedicated architecture options (ASBS) and integration paradigms (zkML, opML), alongside storage, consensus, and decentralized training considerations, while noting limited mature, production-ready deployments. The study shows substantial potential for decentralization, transparency, and governance enhancements in AI, but emphasizes significant challenges in computation, storage, data privacy, and regulatory uncertainty that must be addressed to reach mass adoption. Overall, the work provides a framework for categorizing use cases and outlines practical and theoretical directions for advancing Blockchain X AI research and applications.

Abstract

Blockchain technology and Artificial Intelligence (AI) have emerged as transformative forces in their respective domains. This paper explores synergies and challenges between these two technologies. Our research analyses the biggest projects combining blockchain and AI, based on market capitalization, and derives a novel framework to categorize contemporary and future use cases. Despite the theoretical compatibility, current real-world applications combining blockchain and AI remain in their infancy.

Blockchain and Artificial Intelligence: Synergies and Conflicts

TL;DR

The paper investigates how blockchain and AI can complement or conflict with each other, highlighting both theoretical synergies and real-world obstacles. It uses a bottom-up analysis of Blockchain X AI projects with market capitalization over USD 10 million to develop a four-cluster taxonomy: AI peripheral to blockchain, AI participating in blockchain, blockchain managing AI processes, and blockchain as the core infrastructure for AI. It discusses dedicated architecture options (ASBS) and integration paradigms (zkML, opML), alongside storage, consensus, and decentralized training considerations, while noting limited mature, production-ready deployments. The study shows substantial potential for decentralization, transparency, and governance enhancements in AI, but emphasizes significant challenges in computation, storage, data privacy, and regulatory uncertainty that must be addressed to reach mass adoption. Overall, the work provides a framework for categorizing use cases and outlines practical and theoretical directions for advancing Blockchain X AI research and applications.

Abstract

Blockchain technology and Artificial Intelligence (AI) have emerged as transformative forces in their respective domains. This paper explores synergies and challenges between these two technologies. Our research analyses the biggest projects combining blockchain and AI, based on market capitalization, and derives a novel framework to categorize contemporary and future use cases. Despite the theoretical compatibility, current real-world applications combining blockchain and AI remain in their infancy.
Paper Structure (20 sections, 1 equation, 3 figures, 2 tables)

This paper contains 20 sections, 1 equation, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Blockchain $\times$ AI: Synergies and Conflicts
  3. Synergies Between Blockchain and AI
  4. Conflicts Between Blockchain and AI
  5. Blockchain $\times$ AI: Use Case Study
  6. AI is Peripheral to the Blockchain
  7. AI Participates in the Blockchain
  8. Blockchain Manages AI Processes
  9. Blockchain as the Core Infrastructure for AI
  10. Blockchain Type
  11. Use cases
  12. Blockchain functionality
  13. Storage
  14. Consensus
  15. Decentralized Training, Federated Learning, and Computational Layer
  16. ...and 5 more sections

Figures (3)

  • Figure 1: Market Capitalization of Crypto Assets by Sector. The AI sector has a market capitalization of approximately 20 billion US Dollars (USD), accounting for 0.9% of the total crypto market, which is valued at 2.27 trillion USD, representing an emergent niche.
  • Figure 2: Synergies and Conflicts between Blockchain and AI.
  • Figure 3: Categorization of Use Cases at the Intersection of Blockchain and AI. Two main categories are distinguished: 'AI for Blockchain,' where AI supports blockchain functionalities, and 'Blockchain for AI,' where blockchain technologies enhance AI processes and infrastructure. The horizontal axis shows the growing degree of blockchain integration — from AI as a peripheral tool to blockchain as the core infrastructure for AI.