Scaling and Compute: Pathways to AGI

Experts believe scaling current transformer-based AI architectures could enable AGI. Training models with larger compute, bigger datasets, and innovative architectures may bring machines closer to human-level general intelligence. Constraints include power availability, chip capacity, data scarcity, and latency. Nevertheless, if compute growth continues, frontier AI models could complete increasingly complex tasks, marking steps toward broader autonomy and capabilities resembling early AGI.