Research POV: Yes, AGI Can Happen – A Computational Perspective

This post argues that current AI hardware is significantly underutilized, with training runs achieving only ~20% Mean FLOP Utilization (MFU) and inference utilization in the single digits. It posits that better software-hardware co-design and innovations like FP4 training can unlock substantial performance gains, challenging the idea that AI is hitting a hardware wall. It also highlights that future compute generations have not yet been fully utilized and that current models are already transforming complex workflows like GPU kernel generation.