One hundred watts, not ten thousand — the Green Intelligence starting point
A single AlifZetta inference node draws roughly one-hundredth the power of an equivalent-throughput GPU rack. That is the whole climate argument.
Where the delta comes from
The GPU is a beautiful piece of engineering for matrix multiplication. AlifZetta's inference is graph traversal. Matrix multiplication over billions of parameters costs kilowatt-hours. Graph traversal over thousands of nodes costs nanojoules. Same task class — cited answer to a citizen's question — two orders of magnitude apart in energy.
Why nobody has priced this in yet
The AI industry has been optimising the GPU stack for so long that the alternative feels invisible. When a paradigm changes, the numbers change too. One hundred watts on a Ryzen for sovereign SI is not a downgrade of frontier AI — it is a different architecture that happens to also be one hundred times more energy-efficient.
How this changes deployment reach
If a national grid cannot power a GPU cluster, a national grid cannot host sovereign frontier AI. That is the situation across most of the world. Green Intelligence flips it. Any 15-amp circuit can run a sovereign SI node. That is what population-scale AI actually looks like — and it is only feasible on our stack.
Why we use different words
Where the industry says LLM, we say CLLM. Where it says vector database, we say NEXUS. Where it says Chain-of-Thought, we say PRISM proof-tree. Where it says RAG, we say LATTICE. Where it says trillion-token dataset, we say Smart Router Dataset. Different words because different architecture. Read the whitepaper.