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At close · Tue, Jul 28, 2026
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HomeCryptoMarket StructureBitcoin miners redirect power and billions toward AI a…

Bitcoin miners redirect power and billions toward AI as BTC sours

Miners face weaker payouts after the April 2024 halving and a near 40% year over year drop in daily revenue, while AI is competing for constrained electricity and capacity.

Bitcoin miners are redirecting large amounts of capital and scarce power capacity toward artificial intelligence as Bitcoin’s downturn squeezes mining profitability, according to CryptoSlate. The outlet says miners are increasingly viewing AI infrastructure as a way to monetize reliable electricity and long-term data center capacity when Bitcoin mining economics look less attractive.

CryptoSlate reports that Bitcoin is trading around $64,000, nearly 50% below its October peak, and it links the pressure on miners to elevated network competition and weak transaction fees. The story adds that some large operators are already converting facilities, signing multi-year computing contracts, and, in some cases, pulling back on investment in new Bitcoin mining equipment.

However, Bitwise Europe head of research André Dragosch warns that the timing of the AI pivot could be off. He told CryptoSlate miners may be betting on AI compute demand that takes longer than current investment assumptions, while also arguing that Bitcoin is nearing the end of its downturn, creating a risk that capital and power committed to AI could coincide with improving mining economics.

The economics facing miners are difficult to ignore. CryptoSlate points to Bitcoin’s April 2024 halving, which cut the block subsidy from 6.25 BTC to 3.125 BTC, reducing the number of new coins miners receive, and it also cites VanEck data showing miners’ daily revenue declined nearly 40% year over year to about $28.5 million on average over a 30-day period. It also says elevated network hashrate has kept competition intense, with Hashprice around $30 per petahash per second per day after earlier record-low levels, leaving less-efficient machines at or below breakeven depending on energy costs.

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