Why Jensen Huang’s $500 billion AI financing plan faces a big risk from China
核心要点
- 黄仁勋计划通过华尔街融资5000亿美元建设AI数据中心,将GPU视为长期资产。
- 但该计划面临中国AI发展的竞争风险,GPU使用寿命和二手市场价值存在不确定性。
- 若中国AI进展超预期,可能削弱Nvidia芯片的长期投资价值。

Nvidia CEO Jensen Huang speaks to members of media outside a restaurant in the Hongdae district of Seoul, South Korea, June 5, 2026.
Jensen Huang built the world's most valuable company by pioneering the specialized computer chips behind the artificial intelligence boom.
To keep his vision for the future within reach, the Nvidia founder is now attempting a different kind of engineering: convincing Wall Street investors that those chips are long-term financial assets akin to commercial real estate or toll roads.
His bet hinges on outpacing AI developments in China.
This week, Nvidia unveiled agreements with six of the world's largest asset managers, BlackRock, Blackstone, Apollo, KKR, Brookfield and Goldman Sachs. The goal was to assemble a $500 billion pipeline to finance the construction of data centers and GPU clusters for companies that lack the credit rating or cash to buy millions of dollars of silicon outright.
Key to his plan, which Huang announced during a CNBC segment flanked by the leaders of all six Wall Street firms, is one crucial assumption: that Nvidia's graphics processing units will hold their value over time, behaving more like traditional hard assets than fast-depreciating consumer electronics.
"Nvidia's AI factory platform is really an investable asset, an infrastructure asset," Huang said. "The reason for that is because it's productive, it's revenue generating, it is fungible, it's used by just about every cloud service provider, it runs every AI model."
In standard asset-backed finance, a bank lends money because if a borrower defaults, the bank can repossess the asset — like a building, a warehouse or a cargo ship — and sell it to get their money back. Those physical assets have established secondary markets and can last decades.
But the productive lifespan of cutting-edge GPUs is far from settled.
While new chips power frontier model training, after a few years they are relegated to lower-margin inference work — a shift that directly impacts their resale and collateral value.
"Depreciation is the one key risk here," said Ben Emons, founder of FedWatch Advisors, who structured similar asset-backed loans for IndyMac before joining Pimco as a portfolio manager. Nvidia chips "could depreciate faster than expected," he said.
