Nvidia Taps Wall Street for $500 Billion AI Buildout, Backs GPU Values

The race to build AI computing capacity requires developers to secure vast amounts of power, long-term tenants and investment-grade credit before lenders will fund new campuses. Nvidia’s role at Hut 8’s Beacon Point project shows the chipmaker moving beyond selling GPUs: Chief Executive Jensen Huang is using the company’s balance-sheet strength to support infrastructure built around Nvidia systems. The strategy can accelerate deployment and lock in future chip demand, while increasing scrutiny of financial interdependence across the AI supply chain.

Hut 8 said on July 20, 2026, that a second 15-year, $9.8 billion lease had fully commercialized its Beacon Point campus in Texas, doubling the same tenant’s contracted capacity to 704 MW. The two leases carry a $19.6 billion base-term value and could reach $50.2 billion if all renewal options are exercised. The Financial Times reported on July 28 that Nvidia was behind the tenant and could sublease capacity to neocloud partners that buy its GPUs. The campus will use NVIDIA DSX architecture, with initial energization planned for the first quarter of 2027.

OpenAI’s drive to train and deploy increasingly powerful artificial-intelligence models requires unprecedented computing capacity and electricity. SoftBank’s planned 10-gigawatt data center project in Ohio could become a cornerstone of that expansion if leased by OpenAI. The proposal would also deepen OpenAI’s dependence on Nvidia, the dominant supplier of AI accelerators, while linking chip sales, infrastructure financing and long-term data center commitments on an extraordinary scale.

As of July 28, 2026, Nvidia and OpenAI were discussing a funding arrangement under which the chipmaker could back about $250 billion needed for the Ohio lease. Including Nvidia chips and other development expenses, the project’s total cost could exceed $500 billion, making it one of the largest AI infrastructure transactions ever contemplated. No final agreement has been announced, and the circular flow of financing and equipment spending has renewed concerns about concentrated risk and a potential AI investment bubble.