Pi Network is the top-performing cryptocurrencies from top 100 today, gaining more than 14% in the past 24 hours and climbing to around $0.23. The move marks the token’s first meaningful recovery in months, pushing it to levels not seen since early January.
The rally extends Pi’s recent rebound from its February low near $0.13, meaning the asset has now gained roughly 70% from that bottom. While the price remains far below its all-time high near $3, the latest momentum suggests that sentiment around the project may be improving after a prolonged downtrend.
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AI Computing Experiment
The rebound is tied to i Network’s latest announcement exploring a new utility for its global node network. In a recently released case study, the Pi Core Team detailed a proof-of-concept project demonstrating that Pi Nodes could perform decentralized AI computing tasks.
The experiment was conducted in collaboration with OpenMind, a robotics startup backed by Pi Network Ventures. During the test, a small group of volunteer node operators ran image recognition workloads through a distributed pipeline.
According to the results, tasks were distributed across nodes and processed quickly, with responses returned within four seconds. The test confirmed that Pi’s network could successfully execute AI-related workloads and return usable results to third-party clients.
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421,000 Nodes Could Form a Distributed AI Network
The broader idea behind the initiative is to use the spare computing capacity of Pi’s global node network, which currently includes more than 421,000 nodes representing over one million CPUs.
Since Pi’s blockchain itself does not require large computational power to operate, much of this capacity remains unused. The project aims to potentially convert that idle computing power into a distributed cloud infrastructure for AI companies seeking additional processing resources.
Under this model, node operators could opt in to run AI workloads and receive cryptocurrency payments in return, while the network also provides verified human participants who could assist with human-in-the-loop tasks such as training data validation.
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Questions Remain
Despite the promising proof-of-concept, some analysts remain cautious about the approach. Modern AI training typically relies on large clusters of high-performance GPUs, which are far more efficient for the heavy mathematical operations required by advanced models.
Consumer-grade CPUs – the type used by most Pi nodes – are generally less suited for these tasks. As a result, the network’s potential role may lie more in distributed micro-tasks or human-in-the-loop AI processes rather than large-scale model training.
Still, the experiment demonstrates that Pi’s infrastructure could support certain types of distributed computing workloads, potentially opening new utility for the network.
