As AI and blockchain converge, OpenLedger emerges as a pioneering project at this intersection, aiming to decentralize model training, reward data attribution, and foster agent economies. The platform envisions a transparent, community-owned AI future. In an interview with core contributor Kamesh, we explore OpenLedger’s principles, innovations, and roadmap. Kamesh explains his background in AI/ML R&D, working with enterprises like Walmart and Cadbury. He highlights major industry challenges: AI’s black-box nature, lack of transparency in data usage, and unfair compensation for data contributors. OpenLedger addresses these by ensuring traceability and fair rewards. Positioned as the “blockchain built for AI, ” OpenLedger fills crucial gaps; centralized companies have profited by training AI on user data without consent or compensation. OpenLedger records every training step, data source, and model update openly, enabling scrutiny vital for sensitive use cases such as financial advice and health.

Their Proof of Attribution mechanism exposes data origins behind specific inferences, reducing risks from misinformation. OpenLedger’s ambitious reward system tracks contributions through an on-chain history of datasets and model modifications tied to contributors’ wallets. Each time a network action uses a contribution, that contributor receives a share of the fees transparently and publicly. Technically, OpenLedger offers tools like Model Factory, a no-code platform for fine-tuning language models economically using lightweight LoRA adapters. OpenLoRA enables dynamic plugging of these adapters onto shared base models during inference, overcoming GPU bottlenecks and supporting scalable, cost-efficient model iteration—forging broad participation in AI development. Proof of Attribution (POA) measures and records on-chain the link between data and resulting inferences, ensuring contributors are fairly rewarded with revenue shares traceable by all. To maintain trust amid challenges like model forking or proxy use, OpenLedger hosts models centrally with API-only external access. Currently, the testnet hosts over 4 million active nodes, with more than 10 projects building atop the network, including a former Google DeepMind researcher. Following the recent completion of Epoch 2, upcoming milestones include a Token Generation Event (TGE) and mainnet launch, with full details forthcoming. Kamesh advises developers entering decentralized AI to focus on simplicity—products should be easy to grasp quickly and prioritize smooth, effective solutions over buzzwords. Remember to like and share this story!