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

Artificial intelligence is evolving at an extraordinary pace. Over the last few years, generative AI models like GPT-4 and other large vision-language systems have amazed the world by writing, reasoning, and interpreting images and video. Yet behind the excitement, these systems still rely on a simple idea: predicting the next word or pixel, one step at a time. In late 2025, Meta’s AI research team introduced a radical alternative called VL-JEPA (Vision-Language Joint Embe