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Karpathy highlights Cursor as successful LLM app requiring embedding models, chat models, and diff-application models orchestrated together
Andrej Karpathy· Independent· AI· 2026-06-03· about Cursor
“They orchestrate multiple calls to LLMs. So in the case of cursor, there's under the hood embedding models for all your files, the actual chat models, models that apply diffs to the code. And this is all orchestrated for you.”
Why it matters
Cursor's success depends on a multi-model orchestration stack (embeddings, chat, diff models), indicating that specialized model inference and orchestration services are critical infrastructure for LLM apps.
Investment implication
Companies providing multi-model inference platforms, embedding services, and model orchestration/routing (e.g., LiteLLM, Anyscale, vLLM providers) will see rising demand from LLM app developers.