🗺️Future Roadmap and Expansion Plans: The Evolution of Decentralized AI

Phase 1: Enhancing Decentralization of AI Training

Dandel’s near-term goal is to further decentralize AI training by enabling validators to compete for processing AI training jobs within a market-driven framework. This approach will introduce an open marketplace where validators can bid to execute AI workloads, optimizing computational efficiency and reducing costs for users. By implementing reputation-based mechanisms and performance benchmarks, Dandel will ensure that AI computations are executed with high reliability and security. Validators that consistently deliver high-performance computations will be rewarded with greater visibility in the AI training job marketplace, further incentivizing quality participation.

Phase 2: AI Training-as-a-Service (AITAAS)

A major milestone in Dandel’s roadmap is the establishment of AI Training-as-a-Service (AITAAS), a decentralized solution allowing developers to seamlessly deploy, train, and refine AI models directly on-chain. AITAAS will integrate smart contract-based automation to handle various stages of model training, including dataset verification, gradient updates, and consensus-driven model weight validation. This will significantly lower the barrier to entry for AI developers looking to leverage decentralized computing without requiring deep expertise in blockchain infrastructure. By structuring the training process as an on-chain service, Dandel will ensure immutability, transparency, and verifiability in AI model evolution, effectively eliminating trust concerns associated with centralized AI frameworks.

Phase 3: Fully Autonomous AI Compute Marketplace

Building upon validator competition and AITAAS, Dandel envisions a fully autonomous AI compute marketplace. This decentralized exchange for AI compute resources will function similarly to traditional cloud marketplaces but without centralized intermediaries. Users will be able to specify their AI compute requirements, and validators will compete by offering computational power at varying rates based on supply and demand dynamics. A key component of this phase is the implementation of dynamic pricing models, where AI training costs fluctuate in real-time based on blockchain congestion and validator resource availability. Smart contract-based bidding systems will ensure fair pricing and optimal allocation of compute power, making AI training both scalable and cost-effective.

Phase 4: Expansion to Federated and Collaborative AI Models

The long-term vision of Dandel extends beyond isolated AI training instances to fully decentralized collaborative AI model development. Through federated learning, multiple users will be able to contribute to a shared AI model without exposing their proprietary data. This privacy-preserving approach will enable organizations and individual researchers to collaborate on AI improvements without the risks associated with centralized data collection. Using cryptographic techniques such as secure multi-party computation (SMPC) and differential privacy, Dandel will facilitate a decentralized AI training environment where data remains confidential while contributing to global AI advancements.

Phase 5: AI Model Inference and Deployment Ecosystem

Beyond training, AI models require efficient inference mechanisms to deliver real-time decision-making capabilities. Dandel’s roadmap includes an AI model inference and deployment framework that will enable dApps, enterprises, and developers to utilize trained AI models on-chain. The inference process will be optimized to run efficiently within Solana’s computational constraints, ensuring low-latency and high-throughput AI interactions. Users will be able to access AI models in a permissionless manner, paying for inference computations in Solana (SOL) while benefiting from fee discounts and access priority if they hold DNDL tokens.