[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Jukan [@Jukanlosreve](/creator/twitter/Jukanlosreve) on x 22.6K followers Created: 2025-07-21 07:10:32 UTC Morgan Stanley: AI Spending Shortfall Creates Over $XXX Trillion in Debt Demand Morgan Stanley predicts that total global data center spending will reach approximately $XXX trillion by 2028. Of this, about $XXX trillion will be allocated to hardware (such as chips and servers), with the remaining $XXX trillion for data center infrastructure construction, including real estate, construction costs, and maintenance. On an annual basis, Morgan Stanley projects that data center-related investment demand will exceed $XXX billion in 2028. For context, the total capital expenditure of all S&P XXX companies in 2024 was approximately $XXX billion, highlighting the remarkable scale of AI investment. Despite Surging Spending by Hyperscale Cloud Service Providers, a Massive Gap Remains Over the past few years, AI and data center-related capital expenditures have begun to grow rapidly. Spending by hyperscale cloud service providers alone has increased from approximately $XXX billion two years ago to around $XXX billion in 2024, with the market widely expecting it to exceed $XXX billion in 2025. However, Morgan Stanley analysts note that while these tech giants' internal operating cash flows have been a primary funding source, the surge in investment demand, coupled with considerations for cash reserves and shareholder returns, makes it difficult for their own funds to cover all future expenditures. By 2028, only about $XXX trillion can be covered by corporate self-financing, leaving a significant funding gap of $XXX trillion. Credit Markets Emerge as a Key Driver to Bridge the Funding Gap To bridge this gap, Morgan Stanley sees credit markets playing a crucial future role. Morgan Stanley states that a broad range of credit channels, whether public or private, will become increasingly important in closing this funding deficit. The current market environment is also favorable for the development of this trend. Credit markets have abundant liquidity, and current real yield levels are attractive to long-term investors such as insurance companies, sovereign wealth funds, pension funds, university endowments, and high-net-worth individuals. The investment preferences of these "sticky" funds perfectly align with the demand for scaled, high-quality, and diversified assets in AI infrastructure investment, laying a strong foundation for cyclical capital mobilization. Morgan Stanley has also provided specific forecasts for the structure of key funding channels: - Unsecured corporate bonds issued by the tech industry are expected to provide approximately $XXX billion. - Asset-backed securities (ABS) and commercial mortgage-backed securities (CMBS) based on data centers are projected to provide around $XXX billion. - Asset-based private credit market funding is estimated to reach approximately $XXX billion. - Other funding sources such as sovereign wealth funds, private equity, venture capital, and bank loans total about $XXX billion. Notably, private credit is considered by Morgan Stanley to be the primary funding conduit with the greatest potential. This capital resides at the intersection of expanding asset management scale and a high-interest-rate environment, and is best adapted to the complex, global, and customized funding demands associated with AI infrastructure construction. However, Morgan Stanley acknowledges that predicting the scale of the aforementioned funding channels inevitably involves many assumptions and a degree of speculation. For example, the influx of sovereign wealth funds is difficult to quantify, and long-term funding methods may also shift (e.g., from asset-backed financing to asset securitization). This implies a risk that the statistics for certain funding methods may be "underestimated." Nevertheless, Morgan Stanley emphasizes that credit markets will play an increasingly vital role in supporting the proliferation of AI-driven technologies. XXXXX engagements  **Related Topics** [data center](/topic/data-center) [coins real estate](/topic/coins-real-estate) [chips](/topic/chips) [hardware](/topic/hardware) [debt](/topic/debt) [coins ai](/topic/coins-ai) [morgan stanley](/topic/morgan-stanley) [stocks financial services](/topic/stocks-financial-services) [Post Link](https://x.com/Jukanlosreve/status/1947192450994606444)
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Jukan @Jukanlosreve on x 22.6K followers
Created: 2025-07-21 07:10:32 UTC
Morgan Stanley: AI Spending Shortfall Creates Over $XXX Trillion in Debt Demand
Morgan Stanley predicts that total global data center spending will reach approximately $XXX trillion by 2028. Of this, about $XXX trillion will be allocated to hardware (such as chips and servers), with the remaining $XXX trillion for data center infrastructure construction, including real estate, construction costs, and maintenance.
On an annual basis, Morgan Stanley projects that data center-related investment demand will exceed $XXX billion in 2028. For context, the total capital expenditure of all S&P XXX companies in 2024 was approximately $XXX billion, highlighting the remarkable scale of AI investment.
Despite Surging Spending by Hyperscale Cloud Service Providers, a Massive Gap Remains
Over the past few years, AI and data center-related capital expenditures have begun to grow rapidly. Spending by hyperscale cloud service providers alone has increased from approximately $XXX billion two years ago to around $XXX billion in 2024, with the market widely expecting it to exceed $XXX billion in 2025.
However, Morgan Stanley analysts note that while these tech giants' internal operating cash flows have been a primary funding source, the surge in investment demand, coupled with considerations for cash reserves and shareholder returns, makes it difficult for their own funds to cover all future expenditures. By 2028, only about $XXX trillion can be covered by corporate self-financing, leaving a significant funding gap of $XXX trillion.
Credit Markets Emerge as a Key Driver to Bridge the Funding Gap
To bridge this gap, Morgan Stanley sees credit markets playing a crucial future role. Morgan Stanley states that a broad range of credit channels, whether public or private, will become increasingly important in closing this funding deficit.
The current market environment is also favorable for the development of this trend. Credit markets have abundant liquidity, and current real yield levels are attractive to long-term investors such as insurance companies, sovereign wealth funds, pension funds, university endowments, and high-net-worth individuals.
The investment preferences of these "sticky" funds perfectly align with the demand for scaled, high-quality, and diversified assets in AI infrastructure investment, laying a strong foundation for cyclical capital mobilization.
Morgan Stanley has also provided specific forecasts for the structure of key funding channels:
Unsecured corporate bonds issued by the tech industry are expected to provide approximately $XXX billion.
Asset-backed securities (ABS) and commercial mortgage-backed securities (CMBS) based on data centers are projected to provide around $XXX billion.
Asset-based private credit market funding is estimated to reach approximately $XXX billion.
Other funding sources such as sovereign wealth funds, private equity, venture capital, and bank loans total about $XXX billion.
Notably, private credit is considered by Morgan Stanley to be the primary funding conduit with the greatest potential. This capital resides at the intersection of expanding asset management scale and a high-interest-rate environment, and is best adapted to the complex, global, and customized funding demands associated with AI infrastructure construction.
However, Morgan Stanley acknowledges that predicting the scale of the aforementioned funding channels inevitably involves many assumptions and a degree of speculation. For example, the influx of sovereign wealth funds is difficult to quantify, and long-term funding methods may also shift (e.g., from asset-backed financing to asset securitization). This implies a risk that the statistics for certain funding methods may be "underestimated."
Nevertheless, Morgan Stanley emphasizes that credit markets will play an increasingly vital role in supporting the proliferation of AI-driven technologies.
XXXXX engagements
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