TL;DR: Meta’s Muse, OpenAI’s Dots and Uber’s driver assistant share one bet: the agent speaks first. That moves the hard problem from what to answer to when to interrupt, on which channel, and with what offer. Classic ML and new decision models can solve it.
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; it is the wrong tool to decide whether to send it.
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.
Value then sets both decisions. 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.
Notification and growth teams have solved versions of this for years:
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. Uber’s outcome tracking supplies the labels these models need.
A new model class suits this job. Decision models such as TypeSafe’s Jev and the open-weights Julia 1 from Supersonic Labs 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?
The limits matter. These models judge only the context they are given, so they will not learn that one user ignores mornings;
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.
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.
Jean-marc is a successful AI business executive .He leads and accelerates growth for AI powered solutions and started a computer vision company in 2006. He is a recognized speaker at AI conferences and has an MBA from Stanford.
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Aioga 编辑摘要
Aioga 编辑摘要：作者分析 Meta Muse、OpenAI Dots 和 Uber 司机助手的共同点：智能体主动开口，把难点从写什么转移到何时打断、用哪个渠道。 Aioga 将其归入「行业动态」方向，重点关注它对真实使用和行业竞争的影响。
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