Cango Reports $452.8 Million Net Loss for 2025 Amid AI Infrastructure Pivot
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Key Takeaways
- Cango reported a full-year 2025 net loss of $452.8 million on total revenues of $688.1 million.
- The company mined 6,594.6 Bitcoins in 2025 with an average all-in cost of $97,272 per coin.
- Fourth-quarter results included $252.8 million in combined impairment losses and fair-value adjustments.
- Cango transitioned to a direct NYSE listing and terminated its ADR program during the year.
- The company sold $305 million in Bitcoin in February 2026 to fund its transition to AI infrastructure.
For the fourth quarter of 2025, Cango recorded a net loss of $285.0 million, compared to a net income of $2.4 million in the same period of 2024. Total revenue for the quarter was $179.5 million, with $172.4 million derived from Bitcoin mining and $4.8 million from international automobile trading. Adjusted EBITDA for the quarter was negative $156.3 million.
The company mined a total of 6,594.6 Bitcoins throughout 2025, averaging 18.07 Bitcoins per day. In the fourth quarter, production reached 1,718.3 Bitcoins. The all-in cost to mine per Bitcoin was $97,272 for the full year, increasing to $106,251 in the fourth quarter. As of December 31, 2025, the company had mined a cumulative total of 7,528.4 Bitcoins since entering the industry.
Total operating costs and expenses for 2025 reached $1.1 billion. Fourth-quarter expenses of $456.0 million included an $81.4 million impairment loss on mining machines and a $171.4 million loss from changes in the fair value of Bitcoin collateral. The company attributed the operating loss of $276.6 million in the fourth quarter primarily to the decline in Bitcoin prices.
During the fiscal year, Cango (NYSE: CANG) completed the termination of its American Depositary Receipt (ADR) program and transitioned to a direct listing on the New York Stock Exchange. The company also appointed a new senior management team to oversee its evolution into a global AI infrastructure provider, operating under the EcoHash brand to develop AI inference solutions.
