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We spent $50 to measure Pearl's "AI mining" – 320K GPUs produce zero AI

A new empirical study of Pearl's Proof-of-Useful-Work blockchain found that its network of approximately 320,000 GPU-equivalents, consuming an estimated 112 megawatts of power, produced zero useful AI computation. Researchers determined that the dominant mining software contains no inference code, the verification protocol accepts random matrices by design, and mining is unprofitable across all GPU tiers with returns ranging from -54% to -72%. The study also found that the release of Pearl's mining software caused budget GPU rental prices to rise 38% and utilization to surge from 57% to 94%, displacing legitimate research workloads.

read2 min publishedJun 5, 2026
[Submitted on 3 Jun 2026]


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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.

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