This research aims to present a comprehensive analysis of the evolving synergy between AI and Web3 landscape. It explores current trends and narratives, and maps out key technological stacks across :

1. Infrastructure Layer: general-purpose GPUs, ML-specific GPUs, and GPU aggregators;

2. Middleware Layer: data collection&management&privacy, AI model development, platforms for AI inference and validation.

3. Application Layer further divided into horizontal and vertical segments: bots and agents, AI for intents, blockchain analytics, on-chain games, decentralized social media, DeFi protocol testing, and AI-generated NFTs.

Each area is dissected into subcategories with a focus on investment feasibility, key success factors (KSFs), and notable examples.

The document also provides a nuanced KSF analysis for Web3 use cases in AI and AI use cases in Web3, highlighting the importance of scalability, data security, interoperability, and innovation.

An appendix offers additional insights from industry discussions and references to relevant articles. Intersection with DePIN is also explored.

The seamless integration of artificial intelligence and blockchain technology represents a pivotal advancement in both sectors. This combination is not just a mere fusion of two cutting-edge technologies, but a transformational synergy that redefines the boundaries of digital innovation and decentralization.

As we look toward the future, it is evident that the convergence of AI and blockchain will play a crucial role in shaping various industries. From enhancing data security and integrity to creating new models of decentralized autonomous organizations, this amalgamation holds the promise of more efficient, transparent, and accessible technologies. Particularly in the realm of decentralized finance, the emergence of decentralized AI (DeAI) could democratize access to AI technologies, breaking down the barriers that have traditionally favored large corporations. This could lead to a more inclusive digital economy where individuals and smaller entities can leverage AI tools and services that were previously out of reach.

Furthermore, the integration of these technologies is poised to address some of the most pressing challenges in both domains. In AI, issues like data silos and the immense computational resources required for training large models can be mitigated through blockchain's decentralized data management and shared computational power. In the blockchain space, AI can enhance efficiency, automate decision-making processes, and improve security mechanisms.

Key Narratives Going Forward

1. Fighting Deep Fakes with Blockchain

• Invest in platforms focusing on digital authenticity and origin tracking.

1. Democratizing AI Innovation

• Opportunities in decentralizing AI resources and data platforms.

1. Enhancing Transparency in AI

• Blockchain solutions making AI processes more transparent and auditable.

1. Data Ownership and Privacy

• Startups that enforce data privacy using blockchain in AI applications.

1. Shift towards Open-Source AI

• DeAI Platforms: Investment in decentralized AI access platforms.

• Decentralized AI Agents: Platforms enabling autonomous AI agent interactions.

• Cloud Service Disruption: Decentralized cloud services catering to AI.

• AI in DAOs: Integration of AI in decentralized autonomous organizations.

• Data Management and Security: Collaborative AI research platforms using blockchain.

• Overview: Blockchain-based ecosystems for sharing computational resources across a distributed network. Encompasses physical and virtual resources required for AI and decentralized computation, with overlaps with DePIN.

Subcategories:

• Decentralized General-purpose GPU: Crypto-incentivized marketplaces for general GPU computing, suited mainly for model inference.

  • Examples: Akash, Render Network, Nosana

• Decentralized ML-specific GPU: Crypto-incentivized marketplaces specialized in ML applications, usable for training, fine-tuning, and inference.

  • Examples: Bittensor, Gensyn, BP-FLAC

• GPU Aggregators: Aggregates GPU supply, offering complete solutions for all LLM workloads with ML software overlay.

  • Examples: Io.net, Together

1. Data Collection, Management, and Privacy

• Overview: Addresses AI's need for high-quality data, emphasizing gathering, storing, and securing data, with a focus on privacy and integrity.

Subcategories:

• Automated Data Harvesting: Tools and systems for collecting data autonomously, ensuring a steady flow of information for AI analysis.

  • Examples: Grass, Zettablock, Synesis One

• Decentralized Data Storage: Secure and distributed systems for storing data, leveraging blockchain for enhanced security and immutability.

  • Examples: Filecoin, Arweave, Masa Network, Ocean Protocol, Nevermined_io, Tableland, Bacalhau

• Data Privacy Technologies: Implementing advanced techniques such as ZK to maintain data privacy throughout AI processes. Or Federated Learning: A machine learning approach that enables collaborative model training while keeping data localized.

  • Examples: Synesis One, Proof Market by =nil; (an Ethereum development company)

• Digital Authenticity and Provenance: Ensuring the origin and authenticity of digital assets are verifiable and secure.

• Decentralized Identifiers (DIDs): Creating a verifiable and secure identity layer for data contribution and management.

  • Examples: Worldcoin

2. AI Model Development: Pre-training & Fine-tuning

• Overview: Focuses on developing and refining AI models, with blockchain-based platforms supporting deployment and execution.

Subcategories:

• Decentralized Model Training Platforms: Facilitating collective AI model training on distributed data sets and computational resources.

  • Examples: Bittensor, Gensyn, Together, Modulus Labs, BP-FLAC, Hyper Oracle / HyperspaceAI

• Automated Machine Learning (AutoML): Streamlining the model development process, making AI more accessible to non-experts.

• Collaborative AI Development Environments: Platforms that enable multiple stakeholders to jointly develop and refine AI models.

  • Examples: Bittensor, Ritual.net

• Blockchain-based Model Validation: Utilizing blockchain to independently verify and validate AI training processes.

  • Examples: Hyper Oracle, Bittensor

3. Hosting Models for Inference, Management, and Assetization

• Overview: Involves deploying AI models for operation, including platforms for hosting, managing, and running AI inference and assetizing AI-generated content.

• Subcategories:

  • Decentralized Inference Services: Offering AI model inference capabilities in a distributed and decentralized manner.

    • Examples: Ritual.net, Hyper Oracle, SpectralFi

  • AI Model Management Tools: Providing the necessary tools for maintaining and monitoring AI models in production.

    • Examples: Ritual.net, Hyper Oracle, Fetch.ai

  • Secure Inference with ZKML: Enabling private and secure AI inference using zero-knowledge proofs.

    • Examples: Modulus Labs, EZKL，Giza

  • Decentralized AI Marketplaces: Creating spaces for buying and selling AI-generated content and services.

    • Examples: Bittensor, Nevermined_io

  • AI-Blockchain Orchestration Tools: Middleware that seamlessly integrates AI into blockchain technologies.

    • Examples: Lovo AI (text to speech), Ritual.net, MyShell,

• Overview: Bridges AI and blockchain to enhance applications in various domains, especially within the burgeoning Web3 space.

• Horizontal:

  • Bots and Agents: Agents with identity and ownership infra automatically interact on-chain, such as trading bots for DeFi aggregation and exectution, and agents API to build customized agents.

    • Examples: MyShell, Fetch.ai, futureverse, Autonolas

  • AI for Intents: Creating user friendly interaction, enhancing the connectivity and interaction between different blockchain networks with AI. Overlaps with Intent-centric narratives.

  • AI-driven Blockchain Analytics: Using AI to analyze and interpret blockchain data for insights and trend forecasting.

    • Examples: Zettablock, Nansen, Kaito AI

• Vertical:

  • On-Chain Games: Implementing AI to create dynamic and responsive non-player characters and scenarios.

    • Examples:Parallel Colony

  • Decentralized Social Media: Leveraging AI for personalized content curation and user interaction.

  • DeFi Protocol Testing: Using AI to simulate potential threats for enhanced security testing.

    • Examples: UpshotHQ

  • AI-Generated NFTs: Producing unique NFTs with evolving characteristics powered by AI.

    • Examples: Stability AI

Web3 Use Cases In AI

Leveraging Blockchain, ZK and etc. in AI Deep Learning Process (data, computing, inferencing, etc.)

KSFs:

• Scalability of Decentralized AI Solutions: Scalability of blockchain infrastructure, efficient use of computational resources, and robust data handling capabilities.

• Data Security and Privacy: Advanced encryption methods, secure data sharing protocols, and compliance with data privacy regulations.

• Efficient data storage and retrieval systems, decentralized data governance models, and data provenance tracking.

• Integration of Blockchain and AI Technologies: Compatibility, interoperability of systems, and minimal latency in AI-blockchain interactions.

• Utilization of Zero-Knowledge Proofs (ZK) in AI: Effective implementation of ZK protocols in AI algorithms, maintaining a balance between data privacy and model accuracy.

AI Use Cases In Web3

Leveraging AI in smart contracts, and dApps.

KSFs:

• Autonomy and Decision-making: Advanced AI algorithms enabling bots to make intelligent, autonomous decisions.

• Scalability and Adaptability: Ability to scale and adapt to different blockchain environments and user demands.

• Dynamic Content Generation: AI algorithms create evolving game scenarios and content.

• Creativity and Uniqueness: Producing distinct and appealing NFTs using AI.

• Cross-Platform Compatibility: Functionality across different blockchain platforms and applications.

• Monetization Strategies: Implementing AI to aid in fair and effective monetization models.

• User-Friendly Interface: Simplified user experiences for non-technical users to leverage AI-blockchain benefits.

• User Engagement and Retention: Providing value-added, engaging experiences to retain users.

• Smart Contract Optimization: AI-driven optimization for efficiency and cost-effectiveness in smart contracts.

• Error Detection and Prevention: Advanced algorithms to identify and rectify potential vulnerabilities or flaws.

👇 Google sheet tracking of projects, mkt cap, FDV, investors https://docs.google.com/spreadsheets/d/1b1HQEN3KVwRP4zS7wwQdrMzipyyb_gYKqibR4jaMa3o/edit#gid=0

Inference Compute in AI

The demand for inference computing in AI transcends training, particularly with larger transformer-based models used in NLP and computer vision. Despite the preference for traditional machine learning models on smaller machines, overall inference demands remain higher.

AI on Blockchain and Web3 Challenges

Within the Web3 landscape, the adoption of AI on-chain is limited. Companies grappling with AI integration face ambiguity regarding distributed computing's precise role, leading to a niche market with few firms exploring AI and blockchain integration. Challenges include scalability, bandwidth limitations, and traction struggles. Factors Influencing Compute Platform Choice Beyond cost considerations, factors influencing compute platform choice encompass speed, ease of use, scalability, and performance. Optimization strategies for hardware and software are critical for efficient inference and training tasks in AI.

AI's Potential Use Cases in Web3

The potential applications of AI in Web3 span various domains, from analyzing smart contracts, ensuring transaction understanding, to targeted advertising based on wallet tracking. Enhanced transparency in gaming, payment systems, remittance, and automated governance tools within the Web3 ecosystem presents a significant opportunity for AI. Optimization for AI Workloads Optimization for AI workloads involves hardware-level and software-level enhancements. Specialized hardware and tailored software backbones are pivotal in streamlining AI tasks, enabling more efficient training and inference processes.

Conclusion

The amalgamation of AI and Web3 presents both promise and complexity. AI's potential applications in smart contract analysis, enhancing transparency in transactions, and automated governance tools illustrate the vast possibilities. However, challenges such as scalability limitations and the need for optimized compute platforms persist.

Blockchains and the Future of AI

AI Belongs Onchain — Placeholder

Intersection of Crypto and AI

AI叙事火热，寻找AI与加密交汇的潜力项目 Web3 + AI 赛道全景盘点：130 多个项目，还有哪些未被发掘的宝藏？

Autonomous Agents in Autonomous Worlds

• DePIN (Decentralized Physical Infrastructure Networks): A concept that involves token incentives to encourage users to deploy hardware devices, providing real-world goods, services, or digital resources.

• Two Key Areas:

  • Physical Resource Networks (PRNs) offering services like WiFi, 5G, VPN, geospatial data, and information sharing.

  • Digital Resource Networks (DRNs) providing digital resource infrastructure like broadband, storage, and computational networks. DePIN Tech Stack Overview

• Physical Resource Networks (PRNs)

  • Wireless Networks: Helium

  • Video Streaming: Theta Network

  • Mapping Services: Hivemapper

  • Ride-Sharing: Teleport

  • IoT and MachineFi: IoTeX

• Digital Resource Networks (DRNs)

  • Storage: Filecoin, Arweave

  • GPU Rendering: Render Network

  • Decentralized Computing: Gensyn, Proof Market Use Cases and Target Company Examples

• Decentralized Wireless Networks

  • Use Cases: IoT connectivity, 5G services.

  • Examples: Helium (IoT, 5G networks).

  • KSF: Network coverage, low operational costs.

• Video Streaming

  • Use Cases: Decentralized content distribution.

  • Examples: Theta Network.

This research aims to present a comprehensive analysis of the evolving synergy between AI and Web3 landscape. It explores current trends and narratives, and maps out key technological stacks across :

1. Infrastructure Layer: general-purpose GPUs, ML-specific GPUs, and GPU aggregators;

2. Middleware Layer: data collection&management&privacy, AI model development, platforms for AI inference and validation.

3. Application Layer further divided into horizontal and vertical segments: bots and agents, AI for intents, blockchain analytics, on-chain games, decentralized social media, DeFi protocol testing, and AI-generated NFTs.

Each area is dissected into subcategories with a focus on investment feasibility, key success factors (KSFs), and notable examples.

The document also provides a nuanced KSF analysis for Web3 use cases in AI and AI use cases in Web3, highlighting the importance of scalability, data security, interoperability, and innovation.

An appendix offers additional insights from industry discussions and references to relevant articles. Intersection with DePIN is also explored.

The seamless integration of artificial intelligence and blockchain technology represents a pivotal advancement in both sectors. This combination is not just a mere fusion of two cutting-edge technologies, but a transformational synergy that redefines the boundaries of digital innovation and decentralization.

As we look toward the future, it is evident that the convergence of AI and blockchain will play a crucial role in shaping various industries. From enhancing data security and integrity to creating new models of decentralized autonomous organizations, this amalgamation holds the promise of more efficient, transparent, and accessible technologies. Particularly in the realm of decentralized finance, the emergence of decentralized AI (DeAI) could democratize access to AI technologies, breaking down the barriers that have traditionally favored large corporations. This could lead to a more inclusive digital economy where individuals and smaller entities can leverage AI tools and services that were previously out of reach.

Furthermore, the integration of these technologies is poised to address some of the most pressing challenges in both domains. In AI, issues like data silos and the immense computational resources required for training large models can be mitigated through blockchain's decentralized data management and shared computational power. In the blockchain space, AI can enhance efficiency, automate decision-making processes, and improve security mechanisms.

Key Narratives Going Forward

1. Fighting Deep Fakes with Blockchain

• Invest in platforms focusing on digital authenticity and origin tracking.

1. Democratizing AI Innovation

• Opportunities in decentralizing AI resources and data platforms.

1. Enhancing Transparency in AI

• Blockchain solutions making AI processes more transparent and auditable.

1. Data Ownership and Privacy

• Startups that enforce data privacy using blockchain in AI applications.

1. Shift towards Open-Source AI

• DeAI Platforms: Investment in decentralized AI access platforms.

• Decentralized AI Agents: Platforms enabling autonomous AI agent interactions.

• Cloud Service Disruption: Decentralized cloud services catering to AI.

• AI in DAOs: Integration of AI in decentralized autonomous organizations.

• Data Management and Security: Collaborative AI research platforms using blockchain.

• Overview: Blockchain-based ecosystems for sharing computational resources across a distributed network. Encompasses physical and virtual resources required for AI and decentralized computation, with overlaps with DePIN.

Subcategories:

• Decentralized General-purpose GPU: Crypto-incentivized marketplaces for general GPU computing, suited mainly for model inference.

  • Examples: Akash, Render Network, Nosana

• Decentralized ML-specific GPU: Crypto-incentivized marketplaces specialized in ML applications, usable for training, fine-tuning, and inference.

  • Examples: Bittensor, Gensyn, BP-FLAC

• GPU Aggregators: Aggregates GPU supply, offering complete solutions for all LLM workloads with ML software overlay.

  • Examples: Io.net, Together

1. Data Collection, Management, and Privacy

• Overview: Addresses AI's need for high-quality data, emphasizing gathering, storing, and securing data, with a focus on privacy and integrity.

Subcategories:

• Automated Data Harvesting: Tools and systems for collecting data autonomously, ensuring a steady flow of information for AI analysis.

  • Examples: Grass, Zettablock, Synesis One

• Decentralized Data Storage: Secure and distributed systems for storing data, leveraging blockchain for enhanced security and immutability.

  • Examples: Filecoin, Arweave, Masa Network, Ocean Protocol, Nevermined_io, Tableland, Bacalhau

• Data Privacy Technologies: Implementing advanced techniques such as ZK to maintain data privacy throughout AI processes. Or Federated Learning: A machine learning approach that enables collaborative model training while keeping data localized.

  • Examples: Synesis One, Proof Market by =nil; (an Ethereum development company)

• Digital Authenticity and Provenance: Ensuring the origin and authenticity of digital assets are verifiable and secure.

• Decentralized Identifiers (DIDs): Creating a verifiable and secure identity layer for data contribution and management.

  • Examples: Worldcoin

2. AI Model Development: Pre-training & Fine-tuning

• Overview: Focuses on developing and refining AI models, with blockchain-based platforms supporting deployment and execution.

Subcategories:

• Decentralized Model Training Platforms: Facilitating collective AI model training on distributed data sets and computational resources.

  • Examples: Bittensor, Gensyn, Together, Modulus Labs, BP-FLAC, Hyper Oracle / HyperspaceAI

• Automated Machine Learning (AutoML): Streamlining the model development process, making AI more accessible to non-experts.

• Collaborative AI Development Environments: Platforms that enable multiple stakeholders to jointly develop and refine AI models.

  • Examples: Bittensor, Ritual.net

• Blockchain-based Model Validation: Utilizing blockchain to independently verify and validate AI training processes.

  • Examples: Hyper Oracle, Bittensor

3. Hosting Models for Inference, Management, and Assetization

• Overview: Involves deploying AI models for operation, including platforms for hosting, managing, and running AI inference and assetizing AI-generated content.

• Subcategories:

  • Decentralized Inference Services: Offering AI model inference capabilities in a distributed and decentralized manner.

    • Examples: Ritual.net, Hyper Oracle, SpectralFi

  • AI Model Management Tools: Providing the necessary tools for maintaining and monitoring AI models in production.

    • Examples: Ritual.net, Hyper Oracle, Fetch.ai

  • Secure Inference with ZKML: Enabling private and secure AI inference using zero-knowledge proofs.

    • Examples: Modulus Labs, EZKL，Giza

  • Decentralized AI Marketplaces: Creating spaces for buying and selling AI-generated content and services.

    • Examples: Bittensor, Nevermined_io

  • AI-Blockchain Orchestration Tools: Middleware that seamlessly integrates AI into blockchain technologies.

    • Examples: Lovo AI (text to speech), Ritual.net, MyShell,

• Overview: Bridges AI and blockchain to enhance applications in various domains, especially within the burgeoning Web3 space.

• Horizontal:

  • Bots and Agents: Agents with identity and ownership infra automatically interact on-chain, such as trading bots for DeFi aggregation and exectution, and agents API to build customized agents.

    • Examples: MyShell, Fetch.ai, futureverse, Autonolas

  • AI for Intents: Creating user friendly interaction, enhancing the connectivity and interaction between different blockchain networks with AI. Overlaps with Intent-centric narratives.

  • AI-driven Blockchain Analytics: Using AI to analyze and interpret blockchain data for insights and trend forecasting.

    • Examples: Zettablock, Nansen, Kaito AI

• Vertical:

  • On-Chain Games: Implementing AI to create dynamic and responsive non-player characters and scenarios.

    • Examples:Parallel Colony

  • Decentralized Social Media: Leveraging AI for personalized content curation and user interaction.

  • DeFi Protocol Testing: Using AI to simulate potential threats for enhanced security testing.

    • Examples: UpshotHQ

  • AI-Generated NFTs: Producing unique NFTs with evolving characteristics powered by AI.

    • Examples: Stability AI

Web3 Use Cases In AI

Leveraging Blockchain, ZK and etc. in AI Deep Learning Process (data, computing, inferencing, etc.)

KSFs:

• Scalability of Decentralized AI Solutions: Scalability of blockchain infrastructure, efficient use of computational resources, and robust data handling capabilities.

• Data Security and Privacy: Advanced encryption methods, secure data sharing protocols, and compliance with data privacy regulations.

• Efficient data storage and retrieval systems, decentralized data governance models, and data provenance tracking.

• Integration of Blockchain and AI Technologies: Compatibility, interoperability of systems, and minimal latency in AI-blockchain interactions.

• Utilization of Zero-Knowledge Proofs (ZK) in AI: Effective implementation of ZK protocols in AI algorithms, maintaining a balance between data privacy and model accuracy.

AI Use Cases In Web3

Leveraging AI in smart contracts, and dApps.

KSFs:

• Autonomy and Decision-making: Advanced AI algorithms enabling bots to make intelligent, autonomous decisions.

• Scalability and Adaptability: Ability to scale and adapt to different blockchain environments and user demands.

• Dynamic Content Generation: AI algorithms create evolving game scenarios and content.

• Creativity and Uniqueness: Producing distinct and appealing NFTs using AI.

• Cross-Platform Compatibility: Functionality across different blockchain platforms and applications.

• Monetization Strategies: Implementing AI to aid in fair and effective monetization models.

• User-Friendly Interface: Simplified user experiences for non-technical users to leverage AI-blockchain benefits.

• User Engagement and Retention: Providing value-added, engaging experiences to retain users.

• Smart Contract Optimization: AI-driven optimization for efficiency and cost-effectiveness in smart contracts.

• Error Detection and Prevention: Advanced algorithms to identify and rectify potential vulnerabilities or flaws.

👇 Google sheet tracking of projects, mkt cap, FDV, investors https://docs.google.com/spreadsheets/d/1b1HQEN3KVwRP4zS7wwQdrMzipyyb_gYKqibR4jaMa3o/edit#gid=0

Inference Compute in AI

The demand for inference computing in AI transcends training, particularly with larger transformer-based models used in NLP and computer vision. Despite the preference for traditional machine learning models on smaller machines, overall inference demands remain higher.

AI on Blockchain and Web3 Challenges

Within the Web3 landscape, the adoption of AI on-chain is limited. Companies grappling with AI integration face ambiguity regarding distributed computing's precise role, leading to a niche market with few firms exploring AI and blockchain integration. Challenges include scalability, bandwidth limitations, and traction struggles. Factors Influencing Compute Platform Choice Beyond cost considerations, factors influencing compute platform choice encompass speed, ease of use, scalability, and performance. Optimization strategies for hardware and software are critical for efficient inference and training tasks in AI.

AI's Potential Use Cases in Web3

The potential applications of AI in Web3 span various domains, from analyzing smart contracts, ensuring transaction understanding, to targeted advertising based on wallet tracking. Enhanced transparency in gaming, payment systems, remittance, and automated governance tools within the Web3 ecosystem presents a significant opportunity for AI. Optimization for AI Workloads Optimization for AI workloads involves hardware-level and software-level enhancements. Specialized hardware and tailored software backbones are pivotal in streamlining AI tasks, enabling more efficient training and inference processes.

Conclusion

The amalgamation of AI and Web3 presents both promise and complexity. AI's potential applications in smart contract analysis, enhancing transparency in transactions, and automated governance tools illustrate the vast possibilities. However, challenges such as scalability limitations and the need for optimized compute platforms persist.

Blockchains and the Future of AI

AI Belongs Onchain — Placeholder

Intersection of Crypto and AI

AI叙事火热，寻找AI与加密交汇的潜力项目 Web3 + AI 赛道全景盘点：130 多个项目，还有哪些未被发掘的宝藏？

Autonomous Agents in Autonomous Worlds

• DePIN (Decentralized Physical Infrastructure Networks): A concept that involves token incentives to encourage users to deploy hardware devices, providing real-world goods, services, or digital resources.

• Two Key Areas:

  • Physical Resource Networks (PRNs) offering services like WiFi, 5G, VPN, geospatial data, and information sharing.

  • Digital Resource Networks (DRNs) providing digital resource infrastructure like broadband, storage, and computational networks. DePIN Tech Stack Overview

• Physical Resource Networks (PRNs)

  • Wireless Networks: Helium

  • Video Streaming: Theta Network

  • Mapping Services: Hivemapper

  • Ride-Sharing: Teleport

  • IoT and MachineFi: IoTeX

• Digital Resource Networks (DRNs)

  • Storage: Filecoin, Arweave

  • GPU Rendering: Render Network

  • Decentralized Computing: Gensyn, Proof Market Use Cases and Target Company Examples

• Decentralized Wireless Networks

  • Use Cases: IoT connectivity, 5G services.

  • Examples: Helium (IoT, 5G networks).

  • KSF: Network coverage, low operational costs.

• Video Streaming

  • Use Cases: Decentralized content distribution.

  • Examples: Theta Network.