CleanSpark posts $239M loss as Bitcoin mining revenue falls
CleanSpark reported a $239M net loss in its fiscal Q3, reversing year-ago profit as revenue dropped 30.5% to $138M and results missed estimates. The stock fell about 5.5% on Thursday before partially rebounding in pre-market, highlighting continued fiscal impact from Bitcoin mining weakness and BTC mark-to-market effects.
The quarterly loss was $239M (about $0.89 per basic share). CleanSpark cited a fair value loss on Bitcoin of $224.1M, which made up nearly 60% of the total net loss—showing how sensitive Bitcoin mining equity earnings are to weaker BTC prices. At the same time, BTC holdings rose to $925.2M and cash was $260.3M, indicating ongoing balance-sheet buildup.
New offsetting development: on July 14, CleanSpark signed a 20-year, 175MW AI data center lease at its Sandersville, Georgia campus, targeting about $6.6B in contracted revenue over the initial term. Management said it will commercialize assets for AI and high-performance computing (HPC) while continuing Bitcoin mining operations—potentially reducing earnings volatility over the longer term.
For crypto traders: the near-term market signal is bearish for miners’ equity momentum due to Bitcoin mining revenue pressure plus BTC valuation losses, while the AI/HPC lease is a medium-term stabilizer rather than an immediate earnings reversal.
Bearish
The latest update reinforces that CleanSpark’s near-term earnings are dominated by Bitcoin mining revenue weakness and BTC mark-to-market losses, with the fair value loss on Bitcoin making up nearly 60% of the quarterly net loss. This combination typically pressures miner equities and can amplify sell-offs when BTC is soft. While the 175MW AI data center lease (20 years, ~$6.6B contracted revenue over the initial term) is a positive longer-term diversification step, it does not offset the immediate fiscal impact in this quarter. Hence, the expected price impact on BTC-related equities is bearish in the short term, with the AI/HPC ramp more supportive over time.