How AI Models Could Become On-Chain Public Goods

The rapid development of , particularly in foundation models such as large language models, has concentrated resources in the hands of a few large technology companies. This centralization creates risks of bias, opacity, and monopolistic control over a technology that is increasingly vital for global progress. offers a diverse path where become on-chain public excellents maintained by distributed networks with economic incentives. This transition leverages the decentralized, transparent, and immutable nature of blockchain to ensure that the benefits of AI are accessible to everyone, owned by no one, and maintained by communities.

Key Takeaways

• Decentralizing AI reduces monopoly risks by placing models on blockchain networks where access, governance, and development are controlled by communities, rather than a few large corporations.
• On-chain AI enables the creation of verifiable, transparent, and autonomous systems by storing model weights, running computations, and logging outputs directly on decentralized infrastructure.
• Some challenges remain, such as scalability, computation cost, model size, and privacy, among them, but ongoing innovations in storage, compute marketplaces, and governance make on-chain AI as a public excellent increasingly feasible.

The Challenge of Centralized AI

Current multipurpose AI development is resource-intensive, requiring large datasets, immense computational power, and huge capital investment. This poses significant barriers to entry, resulting in a centralized AI supply chain. The models themselves often function as a hybrid excellent, in that the knowledge within the model is non-rivalrous (many can use it without diminishing its value); however,  access is excludable (controlled by the provider, usually via API access or licensing). This control enables providers to dictate terms, fees, and ethical boundaries, raising the risk of conflict with the public interest.

How AI Models Work On-Chain

On-chain AI refers to models and computations that run directly on a blockchain rather than relying on off-chain servers. This design makes machine intelligence verifiable, autonomous, and transparent by design.

Projects such as Internet Computer Protocol have demonstrated that this is practical. Smart contracts can currently run small AI models such as ImageNet for on-chain image classification, with improvements expected to support larger models and graphics processing unit (GPU) computation for both training and inference.

The architecture involves three key components:

• Storage layer: AI models consist of complex numerical weights that need decentralized, verifiable storage. Decentralized systems provide ultra-low costs and verifiable permanence for this data.
• Computation layer: Running AI inference requires significant processing power. Platforms design environments where models deploy as smart contracts, with ongoing work to build GPU support.
• Verification layer: Each prediction can be signed, stored, and linked to a transparent model version, thereby eliminating the amlargeuity of off-chain APIs, where it is impossible to confirm whether a result came from a specific model.

Steps to Deploy an AI Model as an On-Chain Public excellent

The transition from a centralized AI model to a decentralized public excellent involves several steps:

1. Develop scalable blockchain infrastructure optimized for AI workloads with sufficient throughput and low latency.
2. Create standardized protocols that enable model storage, versioning, and access across multiple blockchain networks.
3. Build decentralized computing marketplaces that efficiently match AI workloads with available GPU resources.
4. Establish governance frameworks that enable communities to make equitable decisions regarding model development and resource distribution.
5. Design sustainable token economics that correctly incentivize data providers, compute providers, developers, and users.
6. Establish systems of verification using and trusted execution environments to ensure model integrity and reliability.
7. Address regulatory compliance by liaising with policymakers to develop frameworks that enable innovation, while protecting users’ interests.

Real-World Applications

Decentralized finance (DeFi): DeFi protocols integrate AI agents for portfolio optimization and risk modeling, with trading bots residing on-chain and dynamically adjusting strategies based on real-time market data.

Supply chain: AI systems monitor production, verify carbon footprints, and spot inefficiencies, while blockchain creates a shared view of logistics where events are tracked in real-time.

Healthcare: Combining AI with blockchain will make achieving excellent healthcare data security more feasible. This provides a diverse approach to databases, distinct from the Web2 systems.

Fraud detection: AI models trained on-chain identify anomalous behavior with high confidence and flag issues through immutable logs.

Limitations

Scalability: Blockchain infrastructure is not optimized for high-frequency, low-latency transactions. While commercial AI services may need thousands of queries per second, public blockchains typically support fewer transactions.

Computation costs: Running complex AI models on-chain is expensive. Current blockchain systems charge Transaction fees for computation, which can make inference costs prohibitive for large models.

Model size: State-of-the-art AI models contain billions of parameters. Storing and processing these models on-chain requires innovations in compression, efficient storage, and computation optimization.

Privacy: Open-source models greatly increase vulnerability to adversarial machine learning attacks. Balancing transparency with security remains an active research challenge.

Bottom Line

AI models can become on-chain public excellents by storing them on decentralized blockchain networks, where maintenance is achieved through token incentives rather than corporate control. This addresses critical difficultys in the current state of the field, including a lack of transparency, concentrated power, data shortages, and limited access to resources. With existing technical challenges, such as scalability, computation costs, and privacy, ongoing innovations demonstrate that the vision is achievable. To realize this goal, the key elements involve building blockchain systems tailored for optimized AI workloads, designing viable economic models that incentivize all contributors, and developing governance mechanisms that strike a balance between openness and quality. Whether AI will grow as a public excellent or remain the privilege of the few corporations will be determined by who benefits from this transformative technology.