China built an AI that works like a human brain and it's 100x faster than current LLMs while using 97% less energy. It’s called SpikingBrain, a family of brain-inspired large models that completely re-engineers how AI processes information. Instead of burning endless energy on continuous matrix multiplications like traditional Transformers, SpikingBrain uses adaptive spiking neurons. It mimics actual biological firing patterns. Neurons only activate when triggered by specific events, meaning the model achieves a staggering ~69% sparsity at the micro level. It doesn't waste compute on silence. It only thinks when it needs to. And results change the game for long-context processing: • Achieves over a 100× speedup in Time to First Token (TTFT) for massive 4M-token sequences. • Matches performance with advanced open-source Transformer baselines while using a fraction of the data. • Built to run and scale stably on non-NVIDIA hardware clusters (like MetaX chips), completely bypassing Western hardware dependency. We spent years trying to brute-force bigger AI models by throwing more electricity and silicon at them. Nature figured out how to do it efficiently millions of years ago.