Content area
The compute power value chain is complex—from the real estate developers that build data centers to the utilities that power them, to the semiconductor firms that produce chips to the cloud service hyperscalers that host trillions of terabytes of data. [...]in February 2025, Chinese LLM player DeepSeek reported that its V3 model achieved substantial improvements in training and reasoning efficiency, notably reducing training costs by approximately 18 times and inferencing costs by about 36 times, compared with GPT-4o.; However, preliminary analysis suggests that these types of efficiency gains will likely be offset by increased experimentation and training across the broader AI market. [...]efficiency gains may not substantially impact overall compute power demand over the long term. [[exhibit 1]] AI demand alone will require $5.2 trillion in investment [[sidebar 2]] We calculate that companies across the compute power value chain will need to invest $5.2 trillion into data centers by 2030 to meet worldwide demand for AI alone. To qualify our $5.2 trillion investment forecast for AI infrastructure, it’s important to note that our analysis likely undercounts the total capital investment needed, as our estimate quantifies capital investment for only three out of five compute power investor archetypes—builders, energizers, and technology developers and designers—that directly finance the infrastructure and foundational technologies necessary for AI growth (see sidebar “Five types of data center investors”).