[2606.04819] The Usefulness Gap in Proof-of-Useful-Work: An Empirical Study of Pearl's cuPOW Protocol

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Computer Science > Cryptography and Security

arXiv:2606.04819 (cs)

[Submitted on 3 Jun 2026]

Title:The Usefulness Gap in Proof-of-Useful-Work: An Empirical Study of Pearl's cuPOW Protocol

Authors:Abhinaba Basu<br>View a PDF of the paper titled The Usefulness Gap in Proof-of-Useful-Work: An Empirical Study of Pearl's cuPOW Protocol, by Abhinaba Basu

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Abstract:Pearl, a Layer-1 blockchain with high-profile AI industry endorsements, markets its Proof-of-Useful-Work (PoUW) protocol as simultaneously securing the network and performing AI inference. We present the first systematic empirical measurement of a deployed PoUW system, finding that Pearl's 24 EH/s network -- representing approximately 320,000 GPU-equivalents consuming an estimated 112 MW -- produces zero useful AI computation. Budget GPU rental prices rose 38% and utilization surged from 57% to 94% following the mining software's public release, displacing legitimate research workloads.

Our measurements span five dimensions: (1) network composition analysis of 8,012 workers shows all have inference-capable hardware, yet the dominant mining software contains no inference code; (2) the verification protocol accepts random matrices by design, confirmed by 44 pool-accepted shares from our open-source miner across NVIDIA, AMD, CPU, and Apple Silicon hardware; (3) statistical distribution checks are trivially defeated by adversarial Gaussian sampling; (4) mining is unprofitable at current PRL prices ($0.21) across all GPU tiers (-54% to -72% ROI); and (5) the mining computation is commodity integer arithmetic portable to any hardware platform, offering no vendor lock-in. These findings quantify the verifiability-usefulness tension identified theoretically by Leinweber et al., providing concrete measurements of its magnitude and economic consequences in a deployed system.

Subjects:

Cryptography and Security (cs.CR); Computers and Society (cs.CY); Distributed, Parallel, and Cluster Computing (cs.DC)

ACM classes:<br>K.4.4; K.6.5; J.4

Cite as:<br>arXiv:2606.04819 [cs.CR]

(or<br>arXiv:2606.04819v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2606.04819

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Abhinaba Basu [view email]<br>[v1]<br>Wed, 3 Jun 2026 12:42:29 UTC (37 KB)

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