Venice AI has selected NEAR Protocol as the infrastructure for its privacy-focused artificial intelligence services, a development that deepens the integration of blockchain technology with confidential AI workloads. The announcement places NEAR at the center of an implementation involving verifiably encrypted inference — and is unrelated to any municipal blockchain initiative by the Italian city of Venice.

The integration is designed to let Venice AI users run AI workloads with privacy protections that can be verified through technical mechanisms, including Trusted Execution Environments and end-to-end encryption. Trusted Execution Environments are hardware-isolated areas of a processor designed to shield code and data while computation runs, and end-to-end encryption is intended to keep prompt content unreadable to the service operator. It gives NEAR a direct use case in private AI infrastructure while extending the relationship between blockchain-based systems and AI applications.

NEAR has increasingly positioned itself as infrastructure for AI-related applications, with a network that supports high-throughput blockchain operations and services focused on confidential computation. The protocol currently describes its infrastructure as supporting 600-millisecond block times, 1.2-second finality, and scalability of up to 1 million transactions per second.

Confidential AI becomes a central focus

Venice AI has been developing privacy-oriented AI services in which sensitive prompts and generated content receive additional protection during processing. Its integration with NEAR's AI infrastructure provides access to private inference capabilities using hardware-enforced security environments.

NEAR's website identifies Venice AI as an existing user of its private inference infrastructure, with prompts processed inside hardware-enforced enclaves. In this setup, the substance of a prompt remains hidden from the underlying infrastructure while it is being processed — the property that distinguishes private inference from standard AI serving. The approach is intended to reduce exposure of sensitive information while still allowing AI applications to draw on advanced computational models.

The latest development builds on an earlier September announcement involving Venice and NEAR. The integration has since expanded into verifiably encrypted AI inference, with both Trusted Execution Environment and end-to-end encrypted models included in the implementation.

The technical model could prove significant for developers and businesses that need AI capabilities while maintaining stronger privacy controls. Rather than relying solely on policy commitments about data handling, the architecture uses technical safeguards designed to provide measurable protection during computation.

NEAR expands its AI infrastructure strategy

The development also fits into NEAR's broader strategy of positioning the protocol as infrastructure for AI agents and applications. Its chain-abstraction architecture is designed to allow applications and AI systems to interact with assets and services across multiple blockchain networks. NEAR also operates Chain Signatures and NEAR Intents for cross-chain execution and transactions — primitives that give AI agents a way to initiate actions beyond a single network.

NEAR has increasingly combined these blockchain capabilities with privacy and AI infrastructure. Its current platform emphasizes private inference, secure agent environments, and confidential transactions as components of its broader technology stack.

For developers, the Venice integration offers an example of how private AI workloads can be connected to blockchain infrastructure without requiring users to expose sensitive information in conventional processing environments.

Venice chooses NEAR. — NEAR Protocol (@NEARProtocol) September 24, 2026

Adoption will determine broader network impact

The partnership may also draw attention to NEAR's network activity as confidential AI applications expand. However, the available announcements do not establish a specific transaction-volume target, nor do they quantify how much additional network activity Venice's implementation will.

That distinction matters because the technical significance of the integration does not automatically translate into measurable changes in token demand or network usage. The longer-term impact will depend on the scale of Venice's AI workloads and the extent to which other developers adopt similar infrastructure.

NEAR has continued expanding its AI and privacy capabilities alongside broader blockchain developments. Its platform now highlights confidential inference, cross-chain transactions, and AI agents as interconnected parts of its infrastructure strategy.

For Venice AI users and developers, the implementation provides a framework for running AI inference with verifiable encryption and hardware-based privacy protections, while giving NEAR a concrete application for its confidential AI infrastructure. The development represents a shift from blockchain being used primarily as a transaction or settlement layer toward its potential role as supporting infrastructure for private and verifiable artificial intelligence services.