Nvidia Partners With Wall Street to Raise $500B for AI

Nvidia has entered into agreements with six of the world's largest financial institutions to secure more than $500 billion in third-party capital dedicated to artificial intelligence infrastructure. The chipmaker is collaborating with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to set up independent compute financing platforms. These specialized platforms will offer dedicated pools of capital to Nvidia's customers, which include frontier AI laboratories, enterprise businesses, and specialized AI cloud providers.

The primary goal of this massive mobilization of capital is to help these customers finance the construction of physical data centers and purchase Nvidia hardware. Nvidia Chief Executive Officer Jensen Huang described the initiative as a milestone that helps "create a new class of productive, investable infrastructure," which he termed AI factories. By establishing these independent financing structures, the partners aim to turn compute capacity and full-stack infrastructure into a recognized, investable asset class for the first time.

This financial strategy comes at a time of unprecedented capital expenditure within the technology sector. According to an International Energy Agency (IEA) report published in April, capital spending by five major tech companies surpassed $400 billion in 2025. That spending is projected to grow by another 75% in 2026, with the vast majority of the investments directed toward data centers. The new financing platforms are designed to ease the burden of these massive capital requirements, allowing companies to access scarce compute resources at scale.

For AI practitioners and developers, this massive influx of Wall Street capital could significantly lower the barriers to accessing high-performance computing resources. Instead of relying solely on venture capital or internal corporate budgets to secure expensive hardware, organizations can leverage these dedicated financing pools to scale their operations. This shift could accelerate the deployment of large-scale AI models and stabilize the supply chain for critical hardware, though the final implementation of the program still depends on the execution of definitive agreements.