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AI cryptomining network's 320,000 RTX 3090-class GPUs allegedly burn 112 megawatts of power on ‘zero useful

Instead of performing real inference or training, the paper, titled The Usefulness Gap in Proof-of-Useful-Work , says the network grinds through random matrix multiplications that merely take the shape of AI math. The self-billed “first empirical measurement of a deployed Proof-of-Useful-Work (PoUW) system” study argues that Pearl’s mining protocol verifies that miners performed matrix multiplication correctly, but does not verify whether that work came from real AI training or inference workloads. This piece sits on 1 source layers, but the real value is showing why the story should not be skimmed past too quickly.

Instead of performing real inference or training, the paper, titled The Usefulness Gap in Proof-of-Useful-Work , says the network grinds through random matrix multiplications that merely take the shape of AI math. The self-billed “first empirical measurement of a deployed Proof-of-Useful-Work (PoUW) system” study argues that Pearl’s mining protocol verifies that miners performed matrix multiplication correctly, but does not verify whether that work came from real AI training or inference workloads. The signal is strong enough to deserve attention, but it still needs to be read as something developing rather than fully settled.

Emerging The topic has initial corroboration, but the newsroom is still waiting on stronger confirmation.
Reference image for: AI cryptomining network's 320,000 RTX 3090-class GPUs allegedly burn 112 megawatts of power on ‘zero useful
Reference image from Tom's Hardware. Tom's Hardware

Instead of performing real inference or training, the paper, titled The Usefulness Gap in Proof-of-Useful-Work , says the network grinds through random matrix multiplications that merely take the shape of AI math. The self-billed “first empirical measurement of a deployed Proof-of-Useful-Work (PoUW) system” study argues that Pearl’s mining protocol verifies that miners performed matrix multiplication correctly, but does not verify whether that work came from real AI training or inference workloads. Pearl swaps Bitcoin's SHA-256 hashing for a scheme it calls cuPOW, which asks miners to compute noised integer matrix multiplications and prove they did so correctly. Tom's Hardware is the main source layer for now, and the rest should be read as a signal that is still widening. On the device side, the useful angle is whether a technical change actually alters feel, lifespan, or upgrade cost in real use.

What is happening now

Instead of performing real inference or training, the paper, titled The Usefulness Gap in Proof-of-Useful-Work , says the network grinds through random matrix multiplications that merely take the shape of AI math. Tom's Hardware form the main source layer behind the core facts in this piece. This is still a developing thread, so the useful part is knowing which source signals are hardening and which ones still need caution. With devices, practical impact usually shows up in battery life, heat, stability, and long-term usability rather than in a few flashy headline numbers.

Where the sources line up

Tom's Hardware is the main source layer for now, and the rest should be read as a signal that is still widening. The self-billed “first empirical measurement of a deployed Proof-of-Useful-Work (PoUW) system” study argues that Pearl’s mining protocol verifies that miners performed matrix multiplication correctly, but does not verify whether that work came from real AI training or inference workloads. Tom's Hardware form the main source layer behind the core facts in this piece.

The details worth keeping

Pearl swaps Bitcoin's SHA-256 hashing for a scheme it calls cuPOW, which asks miners to compute noised integer matrix multiplications and prove they did so correctly. On the device side, the useful angle is whether a technical change actually alters feel, lifespan, or upgrade cost in real use. The readers who should care most are the ones planning to replace a device, buy an accessory, or upgrade a work setup in the next few months. The next step is to see whether the current signals harden into a durable change or fade as a short-lived experiment.

Why this matters most

The signal is strong enough to deserve attention, but it still needs to be read as something developing rather than fully settled. With 1 source layers on the table, the part worth reading most closely is where firm facts meet the market's early reaction. That operation is the same arithmetic that underpins neural network inference and training, which is the foundation of Pearl's pitch that mining and AI compute can be one and the same job.

What to watch next

The next readout is price, device coverage, and whether the change feels real once the hardware reaches users. Patrick Tech Media will keep checking rollout speed, user reaction, and how Tom's Hardware update the next pieces. From 1 early signals, the piece keeps 1 references that are useful for locking the main details in place. That is why the useful reading move is not to stop at the headline, but to compare the promise, the workflow change, and the likely cost before deciding anything.

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