At Connect, Meta pitched Muse for subscriptions and unclaimed funds, but privacy concerns could define adoption.
Meta put its new AI agent Muse at the center of its annual Connect event, signaling that Mark Zuckerberg wants AI pushed across Meta’s consumer products rather than aimed primarily at enterprise buyers.
Muse’s early pitch is practical consumer automation, including finding unclaimed funds and managing subscriptions, but the bigger question is whether users will trust Meta with sensitive data such as credit card details, Gmail access, and everyday financial information.
Sean O’Kane said Muse helped identify unclaimed money in his name, but described that win as more of a “party-trick type thing” than evidence of durable daily use, highlighting the gap between impressive demos and repeat behavior.
Meta’s consumer-first AI strategy stands out as OpenAI and Anthropic focus more heavily on coding and enterprise tools, which matters because Meta’s ad-driven business model could make trust, not capability, the main barrier to adoption.
Meta used its Connect event to make Muse, its new AI agent, the centerpiece of its AI push. The bet is conspicuously consumer-first at a moment when rivals such as OpenAI and Anthropic are putting more emphasis on coding products and enterprise software.
Can Muse overcome Meta’s trust issues? That is the real question hanging over Meta’s latest AI push: the company appears to have a plausible consumer use case and broad distribution through Facebook, Instagram, and WhatsApp, but asking people to hand over financial and personal account access to an ad company is a much harder sell than shipping a slick demo.
Zuckerberg’s message at Connect was straightforward. Meta wants AI features everywhere. That includes Muse, a personal AI agent the company is framing as a mainstream consumer product, down to a Tamagotchi-style device that Meta says is for adults. The strategy cuts against the current grain in AI, where the clearest willingness to pay has shifted toward business tools, software development, and workplace productivity.
That divergence is not necessarily irrational. Meta has never been an enterprise company in the way Microsoft is, or even in the way OpenAI and Anthropic increasingly want to be as they court larger contracts and steadier revenue. Kirsten Korosec said Meta has a long track record of embedding itself in everyday life through Facebook, Instagram, and WhatsApp, and argued that continuing to push AI on the consumer side plays to the company’s strengths.
Still, the early use cases suggest both the appeal and the limit of the idea. Sean O’Kane said one of Muse’s prompts offered to scan for unclaimed funds, and that the service found money in his name, with a check on the way by mail. It is the kind of demo that lands instantly with users because it produces a tangible payoff on day one.
But O’Kane also said the usefulness faded quickly after that. He described the unclaimed-money feature as a one-time win, not a habit-forming reason to keep coming back. For consumer AI products, that distinction matters. A product can be entertaining or even lucrative once and still fail to become infrastructure in a person’s daily life.
The more ambitious version of Muse, according to the discussion, is not one-off discovery but ongoing financial housekeeping. Meta has talked about giving the agent access to services such as credit cards and Gmail so it can cancel unused subscriptions, spot duplicate charges, and handle other chores that already exist in adjacent fintech categories. That idea places Muse in the same broad territory as services such as Rocket Money, except with a general-purpose AI wrapper and Meta’s distribution muscle behind it.
And that is where the trust problem stops being abstract. O’Kane said he was initially surprised that Muse did not immediately plug him into Threads, Instagram, or Facebook when he downloaded it. That made the product feel less invasive at first. Over time, though, he said the system increasingly tried to pull those accounts into its orbit so it could learn more about him, a dynamic that goes directly to the heart of Meta’s reputation problem.
His critique was blunt: Meta’s business is to sell ads. In his view, that business model makes it harder to trust Meta with highly sensitive personal data, even if the company argues that more context can produce more relevant advertising. He contrasted that with Apple, saying he would be more willing to let a Siri-like version of the same product handle sensitive information because Apple is not built around selling him ads.
That comparison is especially uncomfortable for Meta because consumer AI is quickly becoming a platform trust contest, not just a model quality contest. Apple controls the device. Google controls search, Android, and Gmail. Amazon has household billing relationships and an installed base in the home. Meta has reach, yes, but it also has years of baggage around privacy, targeting, and how much of users’ lives it wants to ingest. In enterprise software, procurement departments can negotiate guardrails. In consumer AI, people make a gut call in seconds.
The broader market context also makes Meta’s positioning look both smart and risky. OpenAI and Anthropic have spent much of the recent cycle talking up coding assistants and enterprise adoption. Anthony Ha said part of that reflects economics: the costs are high, valuations are high, and those companies increasingly need revenue at a scale that consumer subscriptions alone may not support. Meta, by contrast, can afford to place a strategic bet on attention and engagement because AI can reinforce the ad machine even if Muse itself is not immediately a major standalone business.
That is the underappreciated angle here. Meta may not need Muse to become a paid software juggernaut in order for the product to matter. If Muse keeps users inside Meta’s apps longer, increases dependence on Meta services, and gives the company more signals about what users want done, bought, or deferred, that has value even before direct monetization. The problem is that users understand this too. The same logic that makes Muse strategically attractive to Meta is exactly what could make consumers wary.
What it means for consumers and the AI market
For users, Muse is arriving at a moment when AI agents are being asked to graduate from novelty to responsibility. Finding forgotten money is fun. Canceling subscriptions is useful. Handling email, finances, shopping, and identity across multiple accounts is something else entirely. Once an AI product asks for access to a credit card, an inbox, and social accounts all at once, consumers start evaluating not just convenience but motive.
Meta has one clear advantage in that race: distribution. Few companies can thread a new AI feature through products already used by large numbers of people globally. But distribution cannot erase category memory. Meta’s struggle is not simply to prove Muse works. It is to prove that a company known primarily for targeted advertising can sit in the middle of a user’s financial and personal life without turning that relationship into another optimization problem.
The hardware angle only sharpens the tension. A cute, Tamagotchi-style device marketed for adults suggests Meta wants Muse to feel ambient and emotionally sticky, not just utilitarian. That may broaden the audience beyond power users, but it also raises the bar for trust. The more personal and always-on the product becomes, the less room Meta has for ambiguity about what data it collects, how long it stores that data, and how tightly those signals are walled off from the advertising stack.
What's next
The next real test is whether Muse can move from headline demo to repeated use in the weeks after Connect. If users keep it at arm’s length unless there is a one-time payoff, Meta will have built another attention-grabbing feature without changing behavior. If people start connecting Gmail, credit cards, and social accounts despite Meta’s reputation, that will signal something more consequential: that convenience has become strong enough to outrun distrust, at least for some mainstream consumers.
Frequently asked questions
What is Meta’s Muse AI agent supposed to do?
Meta’s Muse is being positioned as a consumer AI agent that can handle practical tasks like managing subscriptions and helping users locate unclaimed funds. The goal is to make AI useful inside everyday Meta products rather than limiting it to business software.
Why are people questioning whether they can trust Meta’s Muse?
The trust issue comes from Meta’s long history of privacy scrutiny and concerns about how user data is collected, shared, and protected. If Muse needs access to financial or personal account details, consumers may demand much clearer safeguards before using it.
Can Muse help with subscriptions and unclaimed money?
Yes, that is part of the early pitch around Muse. Meta is presenting the agent as a practical tool for routine consumer tasks, including tracking subscriptions and surfacing money users may be owed.
How is Muse different from enterprise AI agents?
Muse appears aimed first at consumer convenience rather than workplace productivity or enterprise automation. That makes its success depend less on CIO budgets and more on whether everyday users feel comfortable giving it sensitive permissions.
What could stop Meta’s Muse from catching on with users?
The biggest barrier is likely trust, especially if Muse asks for access to financial records, subscriptions, or other sensitive data. Even strong features may not be enough if users worry about privacy, security, or how Meta will use the information.
Did Meta make Muse a major focus at Connect?
Yes, Meta put Muse at the center of its Connect event, signaling that Mark Zuckerberg wants AI woven across consumer products. That emphasis suggests Meta sees AI agents as a mainstream product strategy, not just a side experiment.
