Business · Companies · 2 hrs ago
Bitcoin miners face financial strain as they move into AI data centers
Bitcoin mining companies are moving into AI data centers to seek better returns as mining becomes less profitable.
They are using electricity capacity they already secured to enter a fast-growing AI market.
But building data centers requires heavy spending, and the companies also face the burden of borrowing to fund the projects.
Their shares and finances are now under pressure as those costs mount.
An exchange-traded fund investing in bitcoin mining and digital-power companies has fallen 23% this year.
That reverses a 72% rise last year, which came amid enthusiasm for AI.
The shift has not yet eased the companies' financial strain.
Bitcoin mining companies are turning to AI data centers to improve profitability but are facing falling share prices and financial strain.
High construction costs and borrowing burdens are weighing on the companies’ plans.
The CoinShares Bitcoin Mining & Digital Power ETF, which invests in bitcoin mining and AI data-center companies, has fallen 23% this year.
The ETF had risen 72% last year amid enthusiasm for AI.
- Who
- Bitcoin mining companies moving into AI data centers.
- What
- They face falling share prices and financial strain as they take on high construction costs and borrowing.
- When
- The report was published on November 6, 2026; it cited a 23% fall in the ETF this year.
- Where
- Not stated.
- Why
- The companies are seeking to improve profitability as bitcoin mining becomes less profitable, but AI data-center construction and financing are costly.
This story does not have two clearly opposing sides.
The CoinShares Bitcoin Mining & Digital Power ETF rose 72% amid enthusiasm for AI.
The ETF fell 23%.
The Wall Street Journal reported on the companies’ financial pressures as they move into AI data centers.
- ETF
- CoinShares Bitcoin Mining & Digital Power ETF (WGMI)
- ETF change this year
- Down 23%
- ETF change last year
- Up 72%
- Main financial pressures
- High construction costs and borrowing burdens