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Blackstone’s Jon Gray says AI’s capital appetite will force bigger fundraisings

Crypto Briefing
3 lis 2026, 04:24Crypto
Oryginał
1 min read

Blackstone President Jon Gray predicts tech giants like Meta, Google, and Oracle will need to raise significant equity capital as AI development costs surge.

Building artificial intelligence is becoming increasingly expensive, and Blackstone President Jon Gray believes the costs will continue to rise. Speaking at the Global Financial Leaders' Investment Summit in Hong Kong on November 3, 2026, Gray stated that the AI boom will push tech companies toward more large-scale capital raisings as demand for chips, data centers, and computing power outstrips supply.

Tech Giants Face Big Funding Needs

Gray specifically named Meta, Google, and Oracle as likely candidates for significant equity fundraising to support their expanding AI operations. He also flagged potential initial public offerings from SpaceX and Anthropic that could further strain capital markets. The core issue, according to Gray, is an imbalance between the enormous need for data centers and energy infrastructure and the available supply.

The need for data centers and energy infrastructure is enormous, and the growth in demand for computing power is running well ahead of available supply.

Jon Gray, Blackstone President

Massive Spending Ahead

In earlier remarks, Gray put a figure on the coming spending wave, projecting that capital expenditures by hyperscalers will double to $820 billion, representing about 2.5% of US GDP. Despite recent volatility in tech stocks driven by investor concerns about AI investments, Gray remains optimistic about future AI infrastructure spending.

Market Impact

If major tech companies proceed with significant equity raises, public markets could see substantial new share offerings from some of the most widely held stocks globally. Potential listings from SpaceX and Anthropic would compete for the same capital pool. Gray's emphasis on data centers and energy infrastructure indicates where much of this spending will be directed, as every AI model requires physical computing capacity and reliable power.

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