Nearly all the business media coverage of AI focuses on the eye-popping sums being deployed into data center infrastructure that drives the “compute” coveted by leaders in the AI industry. That “compute” provides the raw processing power required to train, build, and run AI systems. Think of it as the engine behind the technology. The tech community is expected to invest more than $750 billion into data centers this year alone. Estimates for total cumulative spend on the humming warehouses reach over $7 trillion by 2030. Such mind-boggling numbers and the circular financing arrangements to drum up the necessary capital have understandably generated a lot of buzz about a potential bubble comparable to the dot-com bubble.
The development of data centers is a must if we want to capture the productivity gains that AI promises. Overinvestment, though, could not only have a chilling effect on the rapid integration into the global economy but also lead to a calamitous outcome for financial markets. All the vigorous debate is warranted. However, not enough attention is paid to the other kind of infrastructure required to scale AI for highly productive, enterprise-agentic deployments—data infrastructure. Data and databases must be organized, checked for accuracy, and made easily accessible so that an AI agent can both locate a specific data point and use it to complete actual tasks across myriad systems without constant supervision.
