AI Chip Architectures Maps Compute's Cambrian Explosion

The key takeaway is that future AI hardware will be shaped as much by evolving model architectures as by transistor counts.

• –GPU programmability remains the strongest defense against rapidly changing workloads
• –Specialized chips can win dramatically on efficiency when models align with their assumptions
• –Decode-heavy inference shifts the bottleneck toward memory bandwidth and KV-cache movement
• –Interconnects, packaging, compilers, and developer ecosystems increasingly determine real-world performance
• –RSI could invalidate today’s hardware optimizations by introducing radically different computational patterns