Meta launched its new AI agent, Muse, at the company's annual Connect event, pushing a consumer-focused strategy that diverges sharply from rivals like OpenAI and Anthropic. CEO Mark Zuckerberg outlined plans to embed AI across Meta's platforms, betting that the company's deep integration into everyday life will give it an edge over competitors chasing enterprise contracts.
The move came during a week of competing model launches. While OpenAI and Anthropic trained their sights on coding tools and business applications, Meta unveiled a personal assistant designed to live on users' phones - and a Tamagotchi-style AI device the company insists is for adults only. TechCrunch's Equity podcast team unpacked the announcements and what they signal about Meta's AI ambitions.
A consumer bet in an enterprise moment
Kirsten Korosec framed the contrast directly. "How do you put Muse, which is this new personal AI agent that's just been released by Meta and [is] clearly a bet on consumer - how does that fit into what you just described, at least with other frontier AI model companies seeing opportunity and business within enterprise?"
Anthony Ha pointed to the financial pressures driving those competitors. OpenAI and Anthropic face high costs and high valuations, with public offerings on the horizon. Enterprise revenue offers a path to the unprecedented scale of income those valuations demand. Meta, already profitable from advertising, sees a different opening. "If that's where OpenAI and Anthropic are going, then maybe there is more of an opportunity for Meta to make the more consumer-friendly [version and] continue advancing AI on the consumer side," Ha said.
Koroscec noted that Meta's track record supports the approach. Facebook, Instagram, and WhatsApp reach billions of users. Embedding AI into those existing habits plays to an established strength rather than forcing the company into unfamiliar territory.
First impressions: a party trick with a trust problem
Sean O'Kane tested Muse and found a capable but narrow tool. The agent scans for tasks it can perform - one suggestion led O'Kane to unclaimed funds he hadn't known about. "It helped me make some money on my first day, and that was pretty cool," he said. "But that ends pretty quickly, right? That was a one-time shot, but it's not a thing that's repeatable. That's more like a party trick-type thing."
The more ambitious use cases require handing over financial account credentials, credit card details, and email access - the kind of data that lets an agent cancel unused subscriptions or flag double charges. That's where Meta's business model becomes a barrier. "Meta's business is to sell you ads," O'Kane said. "And yes, they'll make the argument that the more they know about you, the more accurate and interesting the ads will be - wake me up when we get to that fever dream."
O'Kane contrasted Muse with Apple's updated Siri, which now performs phone controls that surprised him with their usefulness. He said he'd be far more willing to share sensitive information with Apple, whose business doesn't depend on advertising revenue. Meta's initial restraint - Muse didn't immediately pull in data from his Facebook or Instagram accounts - made him more willing to try the agent, but the app quickly began pushing to connect those services.
For professionals exploring AI Agent Courses, Muse illustrates both the promise and the friction of consumer-facing automation: useful in narrow bursts, but dependent on data access that users may reasonably withhold.
Why this matters for creatives, marketers, and customer-facing teams
Meta's consumer AI push signals where attention - and ad dollars - will flow next. If Muse gains traction, it will reshape how brands reach customers inside Meta's ecosystem. Marketing and communications professionals should watch how the agent surfaces information and makes recommendations, since those mechanics will influence what users see and act on. For customer support and sales teams, the trust question cuts both ways: if users hesitate to share data with Meta's AI, they may also resist AI-driven interactions from brands that rely on the same infrastructure. The pattern to track is whether convenience wins out over privacy concerns - and how quickly.
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