Artificial intelligence has entered a new phase. Over the past decade, massive investment has flowed into building increasingly large models, training on ever-larger datasets, and deploying enormous cloud infrastructure. This has delivered powerful tools, but it has also created a growing problem: AI is becoming too expensive to scale. Training large generative models costs millions of dollars. Running them continuously requires enormous energy consumption, expensive GPUs,

Artificial intelligence investment has exploded over the past five years. Billions of dollars have flowed into building massive data centers, training giant language models, and scaling cloud infrastructure. While this approach has delivered impressive breakthroughs, it has also created a serious financial problem: AI is becoming extremely expensive to build, operate, and scale. The rising costs of computing power, energy consumption, training data, and specialized hardwar

The ROI for datacenters has become one of the most important questions in today’s technology economy. As AI, cloud computing, and digital services expand at record speed, datacenters now sit at the heart of global infrastructure. These massive facilities power everything from email and video calls to high-performance AI models like ChatGPT. Because of this demand, investors, tech leaders, and governments are asking the same thing: What returns can datacenters actually deliver