Are early sign-ups for AI assistants like Muse real demand, or are people just along for the platform ride?

This explores whether early adoption of an AI personal assistant like Muse signals real demand, or whether users are being carried along by the platform the assistant is attached to. First, a limit: none of the notes here study Muse or its users, so the collection can't settle the question for that product. What it can do is give you better tests for telling demand apart from lock-in, and some of them cut against the obvious reading.

Start with demand. One strand of the collection argues that the classic assistant pitch is less popular than it looks. Most people don't want their email and calendar handled for them. They value doing those tasks themselves, and products tend to be built for a narrow group of time-pressed professionals Does the personal assistant model actually serve most users?. If that's right, a burst of early users may be real demand, but from that small segment, and it may not carry over to everyone else. Early users are rarely typical users.

Second, early adoption may tell you more about social networks than about the product. At Microsoft, whether an engineer first tried a new AI coding tool depended more on who they were connected to (especially peers a level removed) than on seniority or role. Whether they kept using it depended on what they actually did with it Do social networks drive adoption of new coding tools?. That points to a useful split: first use spreads through people you know, while continued use reveals real demand. So the question to ask of early Muse users isn't how many there are. It's whether their use survives once the novelty and the peer pull fade.

Third, real demand often doesn't look like the use case the product was built for. People's actual habits are messier than product stories. Assistant sessions more often come after search and browsing than replace them Do people use AI assistants before or after searching?. Deep attachment can grow by accident out of plain practical use: one study of a large online community of people in romantic relationships with AI found those bonds usually started from using it as a tool How do people accidentally develop romantic bonds with AI?. So 'genuine demand' might be real but aimed somewhere other than the assistant's advertised features, and sticky for reasons the product never planned.

On lock-in, the collection points to a less familiar mechanism. The worry isn't only that your data is trapped. As people hand tasks to agents, services start competing to be picked by the agent rather than by the user Will agents compete for attention just like users do?. Meanwhile platforms may 'rent' an LLM's guesses about what users want instead of earning that understanding from real behavior, and those guesses are often made up Can LLMs infer user needs better than owned behavioral data?. Under that model, the platform's hold comes from controlling which services the agent reaches for, not from the user's loyalty. Also, an assistant that completes tasks well isn't necessarily one that protects your privacy or remembers your saved preferences. These turn out to be separate skills Do phone agents succeed at all three critical tasks equally?, so high usage alone can't tell you which of them users are actually relying on.