Why users shouldn’t choose their own LLM models: Choice is not always good

David Loker's article argues against allowing users to choose their own large language models (LLMs) for tasks, suggesting that the ability to select models is more of an evaluation issue than a matter of personal preference. He highlights the hidden costs associated with giving users freedom in model selection and proposes dynamic, data-driven routing as a superior alternative. The expertise in using AI models should reside within the system rather than being left to user discretion, as the latter can lead to inefficient outcomes. This discussion is contextualized with the example of CodeRabbit, which supports a blend of frontier and open models like NVIDIA Nemotron, demonstrating that such an approach can be both cost-efficient and effective in accelerating code reviews.