GPU limits stall AGI hardware roadmap

The "GPU is all you need" consensus is facing a growing backlash from critics who argue that matrix multiplication is a poor substitute for biological efficiency.

• –Energy gap: A human brain operates at ~20W, while current AGI-aspiring clusters require megawatts, highlighting a massive thermodynamic inefficiency in digital CMOS logic.
• –Von Neumann bottleneck: Moving data between memory and compute units on GPUs consumes more energy and time than the actual calculations, a problem that "compute-in-memory" architectures aim to solve.
• –Connectivity vs. Compute: While GPUs excel at parallel math, they lack the dense, 3D interconnectivity of biological synapses, which some argue is the true prerequisite for general intelligence.
• –Brute force vs. Elegance: The industry remains split between those betting on scaling current H100/Blackwell architectures and those who believe AGI requires a "paradigm shift" to analog or neuromorphic chips.