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May every crypto enthusiast reap the rewards of their insights in the AI wave.

Written by TechFlow

The intersection of Crypto and AI is the central theme in this year's crypto market.

With AI technology rapidly advancing, numerous AI-related projects have quickly emerged in the crypto space. Amidst the impressive gains of these projects, it can be easy to feel overwhelmed by the sheer number of options:

What do these AI projects actually do? Which category do they belong to? How can we assess their value?

Though AI is just a two-letter acronym, when combined with blockchain, it encompasses a vast range of business areas far beyond its simple abbreviation.

Understanding the full landscape and subdivisions of Crypto X AI can help us quickly identify projects with significant narrative value and potential.

This guide aims to categorize and outline the various business models formed by combining Crypto and AI, highlighting noteworthy companies and projects in

Basic Logic of Crypto X AI

First, it's crucial to recognize that AI is fundamentally a form of productivity, relying on three key elements:

Computing Power, algorithms (models), and data

On the other hand, Crypto or blockchain acts more as a framework for production relationships, providing an optimal environment to foster AI development.

How does this environment influence the three AI elements? Different answers lead to different directions for crypto projects:

Focusing on these aspects, we can divide the AI landscape into six primary directions. Each direction optimizes one or more of these key elements:

Below, we'll dive into each direction, detailing the business models and highlighting notable companies and projects.

  1. Decentralized Computing and AI Inference Platforms

These platforms utilize blockchain technology to create distributed computing networks, enabling global sharing and use of idle computing resources for AI model training and inference tasks.

By distributing tasks across multiple network nodes, these platforms enhance computational efficiency and reduce the risk of single points of failure.

In this framework, computing power is the main element being optimized.

Representative Projects:

Ritual

Akash Network

Render Network

Bittensor

io.net

Hyperbolic

Gensyn

Prime Intellect

Inference Labs

Nosana

Lilypad

Prodia

Hyperspace

Vanna Labs

Arbius

CUDOS

Flux

AIOZ Network

Aethir

Fulence

iExec

NetMind.AI

OpSec

  1. AI Data and Model Sources

This category focuses on managing and verifying data and AI model sources in a decentralized manner.

Specifically, it emphasizes ensuring the authenticity, transparency, and traceability of data in AI development and the fairness and verifiability of model training. In this framework, data becomes the critical element being optimized, as high-quality, trustworthy data is the prerequisite for training effective AI models.

Blockchain plays a role by using immutable distributed ledgers to record data and model source information, providing a transparent and secure environment where data providers, model developers, and users can verify the authenticity and integrity of data and models.

Additionally, blockchain can automate data usage and model training agreements through smart contracts, ensuring data use complies with the original owner's intentions while promoting legal data sharing and utilization.

Representative Projects:

Rainfall

Numbers

Grass

Koii Network

Flock

Hyperspace

Ocean Protocol

Syntropy

  1. Token-Incentivized AI Applications

This model uses cryptocurrency tokens as incentives to encourage users, developers, and other participants to contribute to AI projects and platforms.

Through token rewards, the community is motivated to actively participate in providing data, developing algorithms, offering computing resources, or training and optimizing AI models. Tokens serve not only as a reward mechanism but also as a medium of exchange within the platform, used for purchasing data, computing power, or professional AI services.

In this model, algorithms and models are the core elements being optimized. The collective effort and resource integration of the community can accelerate the development and iteration of AI algorithms while improving the quality and adaptability of AI models. Additionally, token incentives promote data collection and sharing, as participants can earn token rewards by providing high-quality data, indirectly optimizing the data element.

Representative Projects:

MyShell

ImgnAI

MyPeach

Artificial Liquid Intelligence

IQ.wiki

CharacterX

DeepSouth AI

KIP

  1. On-Chain AI Agents and Security

On-chain AI agents and security involve deploying AI agents on the blockchain to enhance the security and credibility of AI applications.

These AI agents can automatically perform tasks such as transactions, data analysis, and automatic decision-making, with their operations being transparent, traceable, and tamper-resistant when deployed on the blockchain, thereby enhancing the overall system's security.

In this framework, computing power and algorithms/models are the critical elements being optimized because secure AI agents require reliable computing resources to execute complex algorithms and models.

Furthermore, on-chain AI agents can share and utilize data and resources across platforms while ensuring privacy, as ZK (zero-knowledge) related technologies can verify the validity and integrity of data without revealing sensitive information.

Recommended reading: "The Rise of AI Agents: Which Projects Are Worth Watching Early On?"

Representative Projects:

AI Arena

Operator.io

Fetch.ai

Modulus Labs

Delysium

Agent Protocol

Morpheus

Autonolas

Test Machine

DAIN Protocol

Oraichain

  1. AI-Driven Blockchain Marketplaces and Learning Platforms

AI-driven blockchain marketplaces and learning platforms leverage AI technology to enhance blockchain applications, particularly in market trading and online education platforms.

These platforms use AI algorithms to analyze market data, predict trends, provide personalized learning experiences, or automatically match buyers and sellers. AI integration not only improves platform efficiency but also offers users more precise and efficient services.

In this category, algorithms and models and data are considered core elements because AI's effectiveness depends on large, high-quality datasets to train accurate models and provide intelligent services. For example, AI can help analyze and understand user behavior, offering more personalized recommendations in blockchain markets or customized educational content on learning platforms.

Representative Projects:

Bagel Network

SingularityNET

FedML

Numerai

Allora

Upshot

  1. Model Verification

Model verification in the realm of blockchain and AI integration involves using blockchain technology to confirm and ensure the performance, security, and transparency of AI models.

This process leverages the immutable and transparent record-keeping properties of blockchain to verify the training data, algorithm logic, and performance metrics of AI models. The goal of model verification is to establish user trust in AI models, ensuring the decision-making process of the models is traceable and auditable, while safeguarding against malicious tampering or deviations from the intended design.

In this category, algorithms and models become the core elements being optimized. Recording the detailed process of model training and operation on the blockchain provides a transparent evidence chain for every decision made by the AI models, thereby enhancing the models' credibility and reliability.

Additionally, model verification involves using cryptographic technologies like zero-knowledge proofs to protect data privacy while validating model outputs without revealing the internal logic, further enhancing the model's security and privacy.

Representative Projects:

Giza

EZKL

This guide does not cover every AI project within these categories due to space and expertise limitations. However, understanding the subdivisions of the AI track can help investors and researchers quickly determine the business scope of new projects they encounter, providing more reference points for decision-making.

May every crypto enthusiast reap the rewards of their insights in the AI wave.

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