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

In the ongoing debate about the limits of digital computation in advancing artificial general intelligence (AGI), Dan Fu presents an optimistic view in his analysis titled "Yes, AGI Can Happen – A Computational Perspective." While acknowledging existing constraints like hardware limitations and stalled GPU progress, he argues that current AI systems have not yet reached their theoretical potential. Fu highlights the underutilization of hardware, noting that state-of-the-art training often achieves only about 20% Mean FLOP Utilization, with inference utilization even lower. He suggests that significant efficiency gains can be realized through improved software-hardware co-design and innovations such as FP4 training. Additionally, Fu points out that current AI models are based on outdated hardware, and the impact of new, larger clusters of GPUs is still forthcoming. Despite this, existing models are already significantly transforming complex processes, indicating that even without major technological leaps, there is substantial room for growth and improvement in AI capabilities.