Green AI

The GPU bill is not the strategy The easiest way to make reinforcement learning for reasoning models sound impressive is to say: sample more responses, train longer, scale harder. It is also the easiest way to make the finance team develop a facial twitch. Modern reasoning-focused LLMs increasingly rely on reinforcement learning with verifiable rewards: generate multiple candidate answers, score them with a rule-based signal, and update the model toward better reasoning behavior. In mathematics and coding tasks, this has become one of the most important post-training recipes. But it has a small accounting problem, in the same way a leaking ship has a small moisture problem. ...

A chatbot does not need a philosophy seminar to answer “Who directed Oppenheimer?” That sentence sounds obvious. Yet a large part of today’s AI infrastructure behaves as if every user query deserves a carefully staged internal drama: retrieve facts, reason through them, verify the logic, produce a chain of intermediate steps, and finally deliver the answer the system could have produced with a simple lookup. It is impressive in the same way using a crane to move a coffee cup is impressive. Technically capable. Operationally absurd. ...

Tokens are small. That is why they are dangerous. A developer asks an assistant to generate a function, explain a repository, or reason through a failing test. The screen fills with text. Some of it is useful. Some of it is decoration. Some of it is a polite little parade of examples, test cases, alternative implementations, or whitespace that will be thrown away by the next parser in the pipeline. ...