➥ Overcoming Data Scarcity - AI Data Provenance AI cannot reach its full potential without reliable training data. Regrettably, 80% of AI projects fail due to poor or unverifiable data, underscoring the importance of data provenance for developing dependable AI systems. Today, we explore projects that address the multifaceted issue of AI data provenance. Each project tackles a critical aspect, including user-owned data, transparent on-chain records, intellectual property monetization, and data liquidity. Let's dive in! 🧵 … — @vana / $VANA Vana is a decentralized network focused on user-owned data, aiming to transform data ownership, sharing, and monetization. It integrates personal server sovereignty, blockchain coordination, modern cryptography, and tokenized economic incentives, providing users with programmable control over their data usage and permissions via DataDAOs and Proof-of-Contribution. -- — @OpenledgerHQ / $OPN OpenLedger is an AI-focused blockchain that turns data, models, and agents into liquid, tradeable assets. It's EVM-compatible so you can connect existing wallets and contracts with zero friction. Features Datanets for collaborative dataset creation and Proof of Attribution ensuring all AI contributions are traceable and fairly rewarded. … — @oceanprotocol / $OCEAN Ocean Protocol tackles AI's data problem by enabling secure data monetization without compromising privacy. Through Data NFTs and Datatokens, data owners maintain control while allowing AI training access via token-gated permissions. Their Compute-to-Data approach lets AI models train on datasets without exposing raw data, ensuring data provenance while creating sustainable revenue streams for data contributors. … — @getoro_xyz / $ORO ORO addresses AI's private data shortage by creating a fair marketplace where users contribute personal data for AI training while maintaining privacy. Their encryption technology ensures data integrity and provenance throughout the training process. Users earn ORO points for data contributions through quests and social linking, solving the critical problem of incentivizing high-quality, verifiable training data that AI desperately needs. … — @campnetworkxyz / $CAMP Camp Network is a Layer-1 blockchain modernizing IP infrastructure for AI agents. It enables creators to own, share, and monetize their IP while allowing AI agents to train on verified, user-owned data across a global IP registry via its Proof-of-Provenance Camp solves the critical gap between AI's rapid growth and creator protection systems, ensuring verifiable data provenance and fair value capture for all participants in the AI-driven creative economy. ... — @StoryProtocol / $IP Story Protocol tackles AI's $80 trillion IP bottleneck by making intellectual property programmable and tradeable. It provides rights-cleared, specialized datasets for AI training through automated licensing, attribution, and royalty distribution across derivative graphs. Story enables data providers to license uncrawlable datasets permissionlessly while ensuring IP owners get fair compensation. This solves AI's critical need for legally compliant, high-quality training data at scale with their newly launched @psdnai with @a16zcrypto … — @irys_xyz / $IRYS Irys is a complete datachain making training data instantly verifiable and programmable. Irys Offers integrated execution with cryptographic proof of data provenance at disk speed. Enables AI developers to access traceable, rights-cleared datasets while ensuring fair compensation for data creators, solving fundamental verification problems plaguing AI training. … — @LazAINetwork LazAI solves AI's data misalignment crisis through Data Anchoring Tokens (DAT) that make training data verifiable and traceable. Their verified computing framework ensures tamper-proof data provenance using ZKPs and consensus protocols. Enables fair compensation for data contributors while maintaining transparent validation of data sources, directly addressing the reliability issues plaguing AI training datasets. LazAI is incubated by @ProjectZKM, @MetisL2 … — @Lilypad_Tech Lilypad Network is a decentralized compute platform designed to power AI and machine learning workloads through a distributed network of GPUs and other resources. It enables users to run containerized jobs, such as AI model inference, in a serverless environment, while allowing compute providers to monetize their hardware and AI developers to own, deploy and earn from their models … — @grass / $GRASS Grass Network is a decentralized platform where users earn passive income by sharing unused internet bandwidth via user-device or Grasshopper. Built on Solana, it provides public web data for AI training, web scraping, and tasks like price checking or ad monitoring by verified entities. This approach supports AI development with large datasets ethically, avoiding centralized control and privacy issues linked to tech giants.