NEAR Protocol launched AI staking mechanism for private compute access, attracting over 500,000 NEAR tokens committed by users.

NEAR Protocol has built a compelling bridge between cryptocurrency staking and AI infrastructure. Its NEAR AI division launched the "Staking for NEAR AI" feature on July 30, and within roughly two weeks, users had locked up more than 500,000 NEAR tokens to fund confidential AI workloads.

The mechanism replaces credit card payments with wallet-native funding. Rather than paying for AI compute as you would with OpenAI or Anthropic, users stake NEAR tokens and receive recurring compute credits. The principal remains intact, with yield redirected into AI inference costs.

The economics follow a straightforward linear model: staking 500 NEAR generates approximately $5 in monthly AI compute credits—a clean 100:1 ratio between tokens staked and dollar-equivalent credits earned per month. Crucially, these credits are withdrawable and non-expiring, meaning users can reclaim their staked NEAR at any time without losing the base position.

The compute credits fund what NEAR calls "confidential inference"—AI model execution inside hardware-isolated secure environments. A minimum stake of 50 NEAR is required to activate IronClaw, NEAR AI's flagship autonomous agent that handles tasks on behalf of users.

All AI inference and agent operations run inside Trusted Execution Environments, commonly called TEEs—hardware-level secure enclaves that prevent anyone, including the infrastructure operator, from observing the data being processed. NEAR AI's implementation leverages Intel TDX and NVIDIA GPU-based TEE technologies. When a user's AI agent processes sensitive information or executes a task, even the servers running the computation cannot access the inputs or outputs.

The staking launch followed closely behind IronClaw 1.0's debut on July 27–28. The staking mechanism provides infrastructure for these always-on agents, enabling users to keep their AI assistants running continuously without recurring payment friction. IronClaw 1.0 has already demonstrated competitive performance, outperforming rivals in PinchBench and OfficeQA testing scenarios—benchmark suites that measure an agent's ability to handle complex, multi-step tasks in realistic environments.

The model represents a philosophical shift in AI service monetization. Traditional AI companies charge subscription fees or per-token API costs. NEAR's staking model lets users access AI compute without spending down their holdings—a distinction that resonates with a user base already comfortable with staking mechanics.