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Home » SpaceX signs $40 billion debt deal for AI chips, Elon Musk says
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SpaceX signs $40 billion debt deal for AI chips, Elon Musk says

Editor-In-ChiefBy Editor-In-ChiefOctober 25, 2026No Comments2 Mins Read
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Elon Musk said: space x has completed a $40 billion debt financing to purchase graphics processing units (GPUs) used to power its artificial intelligence systems.

In a post on X on Sunday, Musk said SpaceX had completed the financing, adding that the company is investment grade and generates revenue from its Starlink satellite internet and rocket launch businesses.

He added in a subsequent post that the financing will primarily consist of callable debt for graphics processing units and is expected to arrive in the first and second quarters of 2027.

Bloomberg previously reported that SpaceX is in talks with banks and investors to raise $40 billion to buy Nvidia chips, citing people familiar with the matter.

The proposed financing would take debt off SpaceX’s balance sheet and use a special purpose vehicle to maintain its investment-grade credit rating, according to Bloomberg.

According to Bloomberg, Goldman Sachs was leading discussions with investors including Apollo Global Management over the financing structure.

The latest funding highlights the massive capital demands of the AI ​​boom, as technology companies pour billions of dollars into advanced chips and data centers to support increasingly powerful AI models.

SpaceX is aggressively expanding into artificial intelligence this year, calling it “superintelligence” (SI).

The division became the company’s largest business unit, with third-quarter sales soaring ninefold to $7.56 billion from $813 million a year earlier, according to the company’s latest financial results.

Musk also said the bond market has largely cut off access to financing for companies that don’t generate significant revenue outside of AI data centers.

He predicted that more than 200 so-called neo-cloud companies, which provide cloud computing infrastructure specifically designed for artificial intelligence and machine learning workloads, would face widespread failure and consolidation, with only a few surviving.



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