Decentralized Ethereum in the AI Field: Sahara AI Depth Research Report

1. Project Introduction

1. Basic Project Information

Sahara AI is a full-stack, AI-native decentralized blockchain platform. Its core vision is to break the current pattern of artificial intelligence dominated by a few large tech companies, and to build an open AI collaborative economy driven by a global community, where contributions can be precisely traced and profits can be fairly distributed.

The fundamental mission of the project is to provide transparent attribution, complete ownership control, and efficient on-chain monetization channels for AI assets (including datasets, models, and agents) through blockchain technology. Essentially, Sahara AI aims to become the "Ethereum of the AI field," providing a secure and trustworthy underlying infrastructure for the next generation of decentralized AI applications, ultimately returning ownership and value of data and AI innovations to every creator and contributor.

2. Project Development History

• Early 2023: The project officially launched, with the initial idea to create a dedicated "knowledge agent" for experts in different fields.
• Mid-2023: Completed a $6 million seed round led by Polychain Capital.
• 2024: Collaborate with major companies such as Microsoft and Amazon to secure relevant orders.
• August 2024: Secured $43 million Series A funding led by Pantera, Binance Labs, and Polychain Capital.
• From the beginning of 2025 to now: Continuously promoting the implementation of the platform's product matrix. On June 26, the AI Agent Builder and AI Marketplace officially entered the public testing phase.

3. Core Issues and Solutions

Sahara AI has proposed a systematic solution based on blockchain to address key pain points faced by the current centralized AI industry, such as data silos, ambiguous model ownership, high innovation costs, and unfair value distribution.

• Establish a decentralized market that allows anyone to register and trade AI assets on-chain.
• Utilize the immutability of blockchain to record each contribution, achieving transparent and automated profit distribution.
• Provide standardized development tools, lower the development threshold, and promote collaborative innovation globally.

4. Core Team Background

• Sean Ren (CEO): Tenured professor at the University of Southern California, MIT TR35 Innovator.
• Tyler Zhou (COO): Former Investment Director at Binance Labs.
• Technical Team: Members come from Google Brain, Fetch.ai, Ocean Protocol, and hold multiple patents in distributed machine learning.

2. Business Model

1. Platform Architecture and Core Technology

Sahara AI adopts a hybrid blockchain architecture optimized for AI application scenarios, including:

• Four-layer structure: application layer, transaction layer, data layer, and execution layer.
• Hybrid Mode: Key metadata is recorded on-chain, while large datasets and AI models are stored off-chain.
• Core main chain: Developing its own Layer 1 blockchain Sahara Chain, which is EVM compatible and optimized for AI-related computations.
• Security Certification: Has passed SOC2 enterprise-level security and privacy certification.

2. Product Ecosystem

• Data Service Platform (DSP): Crowdsourced data annotation platform.
• Sahara Studio: One-stop development kit.
• AI Exchange: A platform that connects data contributors, model developers, and users.
• Secure execution environment: Ensure the safe execution of AI tasks and privacy protection.

3. Market Entry and Growth Strategy

• User Acquisition: Launch the "Knowledge Airdrop" program and testnet incentive activities.
• Brand and Market: Launch on multiple top-tier exchanges and appear on high-exposure channels.
• Ecological Incubation: Establish specialized incubators to support AI startup projects developed based on its ecosystem.

4. $SAHARA Token Economic Model

• Total supply: 10 billion pieces
• Allocation Plan:
  • Community and Ecological Development (64.25%)
  • Core Contributors (15%)
  • Early supporters (19.75%)
  • Liquidity and Market Stability (1%)
• Token Use: Payment for platform services, staking to participate in network security, exercising governance rights, paying fees, incentivizing contributors.

3. Project Prospects

1. Industry Analysis

The decentralized artificial intelligence market is expected to experience explosive growth, increasing from approximately $2.5 billion in 2024 to over $80 billion by 2030, with a compound annual growth rate exceeding 60%. The main drivers include:

• Awakening of data sovereignty awareness
• Requirements for AI transparency and fairness
• The rise of long-tail AI demand

2. Competitive Landscape Analysis

Major competitors include Bittensor (TAO), Olas (OLAS), and Ritual. The advantage of Sahara AI lies in providing a full-stack solution that covers the entire lifecycle from data processing to model development.

3. Development Plan

Short-term (1-2 years):

• Optimize core technology architecture
• Improve the SaharaStudio development kit
• Promote the official launch of AI AgentBuilder and AI Marketplace

Medium term (3-5 years):

• Achieve breakthroughs in cross-chain technology
• Build a globally leading Decentralization AI ecosystem
• Strengthen market promotion efforts and expand overseas markets

Long-term (more than 5 years):

• Become a leader in the Decentralization AI field
• Explore new application scenarios of AI and blockchain integration
• Build a highly prosperous and diversified ecosystem

4. Summary

1. Core Conclusion

• Accurate positioning, grand vision
• Full-stack solutions constitute a core barrier.
• The team has strong capital strength.
• The token economic model is designed reasonably.

2. Valuation and Investment Advice

• Fully diluted valuation of approximately 6 billion USD, which is at a relatively high level in the industry.
• Investment advice: cautiously optimistic, accumulate on dips

3. Outlook

Sahara AI has the potential to become the underlying operating system of the future decentralized intelligent economy. Its success will validate the feasibility of reconstructing AI production relations through Web3, accelerate the popularization and democratization of AI technology, and give rise to entirely new business models and application scenarios.