Three launches, one pattern
Meta, OpenAI and Uber's latest moves rest on the same idea: the AI agent doesn't wait for the user, it takes the first step. Meta's Muse, announced September 8, is a personal agent that books, emails and keeps working with the app closed. It remembers details, makes unprompted suggestions and checks in for approval, both in its own app and in WhatsApp.
OpenAI's Dots, unveiled September 29, are always-on agents that run "proactive research." They monitor your apps read-only to catch a forgotten invoice or a bug in Slack. They reach you in ChatGPT, Slack and Teams, with text and voice coming.
Uber's driver assistant turns live marketplace signals into advice. Uber's product team described a driver idle for 33 minutes being pointed to a better zone, with evidence. The company's principles are clear: stay always on the driver's side, surface opportunities proactively, and measure whether drivers acted. A hands-free voice version was announced September 24.
From pull to push
Chatbots were a pull interface: the user chose the moment, the channel and the question. Proactive agents invert that. Interrupt too often and users mute you; interrupt too late and the surge has ended or the invoice is overdue. Pick the wrong channel and a good message fails. The LLM can write the message, but it is the wrong tool to decide whether to send it.
Value must beat interruption cost
Every proactive message is a bet. Send only when its expected value to the user exceeds the cost of interrupting them. Value has four parts: how much is at stake, how likely the user is to act, how fast the opportunity expires, and whose value it is — the user's or the platform's. High-value, expiring messages go now; modest ones wait for a digest; the rest are never sent. And the more a message is worth, the more intrusive a channel it has earned.
Where decision models fit
Notification and growth teams have solved versions of this for years: uplift models estimate whether a nudge causes the action, contextual bandits learn the best moment and channel for each user, and receptivity models predict whether the user can engage right now. Channels have a price of admission: an in-app card costs little, chat costs more, SMS more still, and a voice call is reserved for urgent, hands-busy moments.
A new model class suits this job. TypeSafe's Jev and Supersonic Labs' open-weights Julia 1 return no text, only a typed judgment: a choice from a short list, a score, or a yes/no, each with a probability. Julia 1 runs on a CPU, from about 33 ms per decision. An always-on agent evaluates far more triggers than it sends, so each candidate can get cheap, structured questions before any LLM call: Is this worth an interruption? How valuable, how urgent? Send now, wait, batch or drop? Card, chat, SMS or voice?
Limits and caveats
These models judge only the context they are given, so they will not learn that one user ignores mornings. On the other hand, an agent that speaks first is a powerful distribution channel. Meta says it is exploring commerce in Muse and has already launched Muse for Small Business; OpenAI announced Dots alongside a new $500 monthly tier; Uber has delivery and other services to promote. But an offer is just another message; it must clear the same bar, measured on value to the user. Once users suspect the agent is selling instead of serving, every message loses credibility.
The takeaway
All three companies have strong models. The race will be won on judgment about attention: speaking rarely, at the right moment, in the right place, on the user's side. Companies with years of notification data may have a head start.
