I think that is right. The sweat spot is twofold: 1) A replacement for general search on a topic where you have limited familiarity that can give you an answer for a concise question, or a starting point for more investigation or 2) For power-user use cases, where there already exists subject matter expertise, elaboration or extrapolation from a clear starting point to a clear end state, such as translation or contextualized exposition.

The problem comes with thinking you can bridge both of those use cases - vague task descriptions to final output. The work described in the article of getting an LLM itself to break down a task seems to work sometime but struggles in many scenarios. Products that can define their domain narrowly enough, and embed enough domain knowledge into the system, and can ask the feedback at the right points, and going to be successful and more generalized systems will either need to act more like tools rather than complete solutions.