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financing

JPMorgan has raised its forecast for AI infrastructure investment to $5.5 trillion, as giants like NVIDIA are turning to debt financing

J.P. Morgan strategist Tarek Hamid and his team have raised their forecast for total investment in artificial intelligence infrastructure by 2030 to $5.5 trillion in a recent research report, an increase of $400 billion from their prediction last November. The bank noted that in this investment race for super-scale data centers, approximately $4.1 trillion will come from debt financing, with loans covering an average of 85% of total project costs, indicating that AI capital expenditures have shifted to a debt market-centric financing model.Since last November, global bond issuance related to AI and data centers has exceeded $300 billion. The latest typical case comes from chip giant NVIDIA, which completed the pricing of a $25 billion investment-grade bond issuance this Monday, marking its return to the bond market for the first time in five years. This issuance was conducted in seven tranches (with maturities ranging from 2 to 30 years) and attracted oversubscription of up to $85 billion, ultimately increasing the issuance size by 25% from the initial target.The research report emphasizes that although tech giants like NVIDIA, Alphabet, and Amazon are generating substantial cash flow from the AI boom (with NVIDIA estimating free cash flow exceeding $200 billion this fiscal year), these giants still choose to issue hundreds of billions of dollars in bonds. This indicates that such bond issuance is not due to a "lack of financing," but rather that the credit market is confirming the pricing of AI assets.

Energy company TAR completes $27 million seed round financing to address power issues in data centers during the AI era

Green energy infrastructure startup TAR announced the completion of a $27 million seed round financing to develop modular "plug-and-play" power systems for data centers, aimed at addressing the power and deployment bottlenecks faced by data centers in the AI era.According to reports, the solution combines solar energy, wind energy, battery storage, and natural gas backup units to achieve nearly round-the-clock (24/7) local power supply capability, reducing reliance on the public grid and thus bypassing issues such as grid access queuing, approval delays, and power price fluctuations. TAR's co-founder stated that the core idea is to significantly compress the deployment cycle of energy systems through factory prefabrication, pre-assembly, and pre-testing, enabling data centers to achieve "rapid go-live" capability.In pilot projects, the system can provide approximately 10 MW of stable power supply and plans to deploy over 200 MW of normal load capacity by 2027. The company noted that its first customer is an undisclosed "neocloud" service provider, aiming to provide a faster energy deployment path for AI computing infrastructure.In terms of the economic model, TAR stated that its solution does not aim for costs below those of traditional grids but prioritizes solving the "speed issue." Its off-grid energy system can be deployed in about three months, avoiding the time costs associated with grid access and land restrictions. As the demand for AI computing continues to grow, power supply has been identified by multiple studies as one of the main bottlenecks for data center expansion. Industry analysis suggests that the "off-grid energy + modular data center" model is becoming a new direction in the competition for AI infrastructure.
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