Can bigger-is-better 'scaling laws' keep AI improving forever?

The concept of 'scaling laws' in AI refers to the observation that larger models and datasets tend to improve performance. However, whether this trend can continue indefinitely is uncertain. As models grow, they require exponentially more computational resources, data, and energy, making it increasingly challenging to sustain this growth. Moreover, the law of diminishing returns may eventually apply, where further scaling yields minimal improvements.