This weekend, Andrej Karpathy, the former director of AI at Tesla and a founding member of OpenAI, decided he wanted to read a book. But he did not want to read it alone. He wanted to read it accompanied by a committee of artificial intelligences, each offering its own perspective, critiquing the others, and eventually synthesizing a final answer under the guidance of a “Chairman.”To make this happen, Karpathy wrote what he called a “vibe code project” — a piece of software written quickly, largely by AI assistants, intended for fun rather than function. He posted the result, a repository called “LLM Council,” to GitHub with a stark disclaimer: “I’m not going to support it in any way… Code is ephemeral now and libraries are over.”Yet, for technical decision-makers across the enterprise landscape, looking past the casual disclaimer reveals something far more significant than a weekend toy. In a few hundred lines of Python and JavaScript, Karpathy has sketched a reference architecture for the most critical, undefined layer of the modern software stack: the orchestration middleware sitting between corporate applications and the volatile market of AI models.As companies finalize their platform investments for 2026, LLM Council offers a stripped-down look at the “build vs. buy” reality of AI infrastructure. It demonstrates that while the logic of routing and aggregating AI models is surprisingly simple, the operational wrapper required to make it enterprise-ready is where the true complexity lies.How the LLM Council works: Four AI models debate, critique, and synthesize answersTo the casual observer, the LLM Council web application looks almost identical to ChatGPT. A user types a query into a chat box. But behind the scenes …
