After the initial surge of attention, practical developments in AI and blockchain concentrate on infrastructure rather than speculative tokens.

Infrastructure behind the headlines

Most meaningful progress today focuses on systems and interoperability that enable agents, devices and data marketplaces to cooperate reliably.

State of blockchain agents market

The market of blockchain-enabled agents cooled after its peak, yet experimental deployments and niche use cases continue to show steady activity.

Agent economy stack and core projects

Several initiatives are building the software and protocols required for an agent economy, aiming to standardise discovery, payments and reputation mechanisms.

• Identity and reputation layers for agent verification and trust scoring.
• Payment rails that enable microtransactions between autonomous services and devices.
• Data marketplaces that standardise access and compensate contributors for labelled datasets.
• Orchestration layers that connect on-chain agreements with off-chain compute resources.

What blockchain can solve for AI

Blockchain can provide audit trails, immutable provenance and tokenised incentives, improving traceability and participation in shared AI workflows.

At the same time, on-chain approaches can help protect sensitive data through selective disclosure and cryptographic access controls.

DePAI, DePIN and robotics as next steps

The concept of DePAI parallels DePIN in applying decentralised infrastructure models to AI, while robotics introduces higher demands on latency and safety.

Practical limits: speed, security and regulation

Blockchain integrations face real constraints around transaction throughput, secure off-chain computation and evolving regulatory frameworks across jurisdictions.

In many deployments, hybrid architectures keep performance-critical inference off-chain while recording attestations and payments on-chain.

Where blockchain is most useful

Blockchain shows the most value in scenarios requiring provenance, shared incentives, and cross-provider settlements for data and model usage.

Overall, the field is shifting from speculative narratives to pragmatic engineering, aligning decentralised primitives with concrete AI operational needs.