At its Connect event, Meta introduced Muse, a personal AI agent and a consumer-oriented companion device, signaling the company’s intention to integrate AI features broadly into its products. In the latest episode of TechCrunch’s Equity podcast, hosts Kirsten Korosec, Sean O’Kane and Anthony Ha discussed how Meta’s consumer bet contrasts with moves by other AI companies such as OpenAI and Anthropic.
Choosing consumers over enterprise
While several frontier AI companies have emphasized coding tools and enterprise products, Meta has doubled down on a consumer strategy. Alongside Muse, Meta showed a Tamagotchi-style device aimed at adults, which the company says is for adult users only. Podcasters noted that this consumer focus may play to Meta’s strengths: the company already reaches large audiences through Facebook, Instagram and WhatsApp and has a track record of embedding services in everyday life.
Early impressions: a one-off win, not yet a daily utility
Sean O’Kane, who has tried Muse, described a concrete early benefit: Muse suggested scanning for unclaimed funds in his name and found some, leading to a forthcoming check in the mail. He framed that outcome as a useful but largely one-time payoff — a “party-trick” rather than a feature that guarantees continued, repeated use.
The trust wall: sensitive data and an ad-driven business model
A recurring concern in the discussion is user trust. Muse recommends tasks that would require sensitive personal data (for example, credit-card details or Gmail access) to perform functions such as cancelling unused subscriptions or spotting duplicate charges. O’Kane said that when he first installed Muse it didn’t immediately pull in his Threads, Instagram or Facebook context, which made him more comfortable using it initially because he didn’t feel Meta “already had everything” on him. However, the app then prompts users to connect accounts and import context so it can learn more about them — a dynamic that will prompt hesitation for some users.
Market positioning and comparisons
Anthony Ha suggested that OpenAI and Anthropic are increasingly orienting toward enterprise customers — partly because of high costs and the need to scale revenue — whereas Meta may see an opening to focus on consumer-facing tools. Kirsten Korosec emphasized that Meta’s strength lies in integrating into everyday consumer routines, so a consumer-first AI strategy is a plausible play for the company.
Conclusion
Muse’s debut highlights a split in the current AI landscape: some companies prioritize enterprise monetization, while others double down on mass-market consumer experiences. Early usage of Muse shows it can deliver striking, one-off benefits, but its long-term adoption will hinge on whether users are willing to share sensitive data with a company whose primary business model is selling ads. The conversation on TechCrunch’s Equity podcast underscores that Muse is both an intriguing consumer experiment and a reminder that data trust remains a central barrier to broader adoption.
