CSPs, or Cloud Service Providers, are companies that provide computing resources, storage space, databases, and AI computing power to businesses and individuals via the internet. Simply put, CSPs represent the "sharing economy of IT infrastructure"—your files reside in the cloud, your company website runs in the cloud, and AI applications rely on the cloud to access intelligent capabilities.
Statistics show that CSP capital expenditures have significantly exceeded 60% of their operating cash flow. In simpler terms, these companies are directly investing a large portion of their operating profits and financing into AI computing power development. By 2025, Amazon had allocated over 90% of its cash flow to AI computing power investment, Microsoft over 70%, and Google over 50%. This rapid expansion of capital expenditures is putting severe strain on the balance sheets and the health of operating cash flow for these companies.
AI chips are the fundamental building blocks of AI clouds. Nvidia, with its powerful GPU product portfolio, has become a core supplier in the global AI computing power market. Data shows that leading global cloud service providers contribute 50% of Nvidia's data center revenue, meaning that more than half of the world's high-end AI chips are used to support CSP AI service outputs.
This highly concentrated supply chain structure is profoundly impacting the profit margins of cloud service providers (CSPs). Chip suppliers maintain absolute dominance in pricing and supply-demand structures, while cloud service providers struggle to make a profit under the pressure of high procurement costs and intensive depreciation. For example, the Nvidia Blackwell B200 costs $30,000 to $40,000 per unit; in hyperscale AI clusters, GPUs alone can account for nearly half of the total investment.
Beyond chip issues, electricity has become a more fundamental limiting factor. AI computing clusters require far more power than traditional data centers. Goldman Sachs predicts that by 2027, the power density of a single AI server rack will be 50 times that of a typical cloud server five years ago.
The expansion of the US power grid is far slower than the construction of data centers. Building a large data center takes an average of two years, while building new high-voltage power lines takes five to ten years. Furthermore, Virginia's major power supplier has received orders for up to 47 gigawatts of data center power over a period spanning several years—equivalent to the total power generation of dozens of large generator units.
A Deloitte report indicates that by 2035, the electricity demand for AI data centers in the United States may increase more than 30 times compared to 2024. Grid stability and the policy environment have become primary considerations in data center site selection decisions. In the next five years, at least 50 to 80 gigawatts of capacity will be needed to meet the increased electricity demand for US AI infrastructure.
Under the triple pressure of rising capital expenditures, chip procurement, and electricity costs, cloud vendors, while experiencing rapid growth in AI revenue, are facing significant downward pressure on profit margins. Due to a substantial increase in equipment depreciation and amortization expenses, cloud business profit margins are beginning to be under pressure, while market concerns about cash flow are also deepening.
In contrast to the rapid investment in AI, downstream enterprises' willingness to pay for AI applications has not yet met expectations. Many companies report that token demand is lower than expected, and many AI application scenarios are still in the experimental stage.
To cope with the current financial structure, some second-tier CSPs have begun to use debt financing to supplement capital investment, seeking a balance between debt and profit margins.
The shift from traditional cloud architecture to AI-native technology systems places higher demands on the technological iteration capabilities and R&D organization capabilities of CSPs. Gartner predicts that by the end of 2026, nearly four-fifths of global CSPs will have deployed AI agents for production environment operations. This shift signifies that the industry will move from traditional, simplified architectures to entirely new service frameworks.
Looking ahead, the AI-driven cloud computing industry will undergo profound adjustments. On one hand, Morgan Stanley predicts that 2027-2028 will be a "high-risk period" for US data center development, with power shortages potentially reaching 10% to 20%. On the other hand, global AI spending is expected to exceed $2 trillion in 2026, and while the growth rate of CSP capital expenditures has slowed, it will still remain above the high base of 2025. The current common business cycle model for CSPs is to acquire infrastructure scale through large-scale AI investment, lock in future returns through long-term customer contracts, and then gradually amortize investment costs using cloud revenue.
This implies that the composition of cloud service costs is undergoing profound changes for ordinary users. When selecting and evaluating cloud services, it is necessary to take into account the comprehensive capabilities of CSPs in addressing the above challenges, including the sustainability of computing power supply, the evolution path of the technical architecture, the foresight of energy deployment, and the ability to independently control the chip ecosystem.
