Sahara AI Launches Crypto-Reward Data Labeling Platform to Fuel AI Training with $450K in Token Bounties

Sahara AI, a Los Angeles-based company, has launched a data labeling platform that compensates contributors in cryptocurrency for tasks critical to training artificial intelligence systems. The Data Services Platform (DSP) operates on a bounty model, offering over $450,000 in token rewards at its launch. Users annotate images, transcribe audio, or evaluate AI-generated text, transforming traditionally low-paying data preparation work into a decentralized gig economy. The platform emphasizes quality control through automated checks, peer review, and reputation scoring, while also introducing shared data ownership to incentivize long-term participation.

Unlike conventional bug bounty programs focused on software vulnerabilities, Sahara AI’s model prioritizes generating high-quality labeled datasets, which are foundational for training accurate AI models. Users can earn rewards in $SAHARA tokens, stablecoins like USDC, or partner-specific tokens. Tasks are categorized into three types: Enterprise Tasks (paid in $SAHARA for large-scale labeling projects), Dual-Reward Tasks (combining $SAHARA and partner tokens), and Community Tasks (granting ownership stakes in datasets for future revenue sharing). This structure aligns with broader trends in crypto-native platforms, such as Immunefi for security bounties and Gitcoin for open-source funding, which leverage decentralized incentives to crowdsource expertise.

Quality assurance mechanisms are central to the platform’s design. Sahara AI employs honeypot questions—hidden tasks designed to detect malicious actors—and AI-driven tools to identify users exploiting automated systems for fraudulent contributions. Users found cheating face temporary or permanent bans. The platform also requires contributors to stake tokens as a deterrent against Sybil attacks, where fake identities are used to manipulate rewards. These measures aim to balance accessibility with accountability, ensuring datasets meet rigorous standards for AI training while minimizing bad-faith participation.

The initiative reflects growing demand for curated training data in the AI sector, where unstructured datasets often hinder model accuracy. By compensating contributors in crypto, Sahara AI taps into a global workforce, sidestepping traditional employment barriers. CEO Sean Ren, a USC computer science instructor, noted that the model not only democratizes access to AI-related work but also aligns incentives between data providers and users. The platform’s beta phase, which ran for months with select partners, has transitioned to an open-access format, inviting broader participation. This approach mirrors the evolution of open-source software funding models, where decentralized contributions accelerate innovation while preserving transparency.

While the crypto-gig economy remains nascent, Sahara AI’s focus on data ownership and recurring revenue streams from datasets marks a departure from one-time bounty rewards. By enabling contributors to retain partial equity in the datasets they build, the platform fosters long-term engagement, potentially reshaping how AI training data is produced and monetized. As enterprises increasingly prioritize data quality, platforms like DSP may become critical infrastructure for the next wave of AI development, bridging the gap between decentralized labor and institutional AI needs.