Meta's Muse AI agent has racked up more U.S. and Canadian downloads and daily active users in its first weeks than ChatGPT managed over the same period after its own mobile debut, according to new estimates from app-analytics firm Appfigures.
Key takeaways
Meta's Muse has outpaced ChatGPT's early mobile download and daily-active-user counts in the U.S. and Canada, per Appfigures estimates.
Muse is an AI agent — meaning it can take multi-step actions autonomously, such as reading your Messages and Calendar — not just a chatbot.
The growth gap reflects Meta's distribution advantage: Muse ships inside an ecosystem of billions of existing users across Facebook, Instagram, and WhatsApp.
For AI-art creators, Muse's rapid adoption signals that agentic, context-aware AI assistants are moving from niche to mainstream faster than most observers expected.
The comparison is to ChatGPT's early mobile period, not its current scale; OpenAI's app remains the larger product by total installed base.
What Muse actually is — and why the comparison matters
Muse is Meta's agentic AI assistant — an AI that doesn't just answer questions but can autonomously execute multi-step tasks by reading connected data sources like your Messages, Calendar, and Notes. As Charmloop previously reported, the Mac app's deep system access raised immediate questions about transparency, since Muse struggled to clearly describe its own capabilities when asked directly.
The Appfigures comparison is to ChatGPT's early mobile window, not its current scale. OpenAI's app has years of compounding growth behind it. The point of the data, according to TechCrunch, is the velocity: Muse is acquiring users faster at launch than ChatGPT did, which is a meaningful signal about consumer appetite for agentic tools right now.
Appfigures data showing Muse outpacing ChatGPT's early mobile download trajectory in the U.S. and Canada.
The growth gap is not purely a product story. Meta's advantage is structural: Muse launches into an existing ecosystem of billions of active accounts across Facebook, Instagram, and WhatsApp. ChatGPT, by contrast, had to build its user base from scratch when it hit mobile. That context matters when reading the headline numbers — Muse had a runway that OpenAI simply didn't.
Still, raw adoption speed has compounding effects. A larger early user base means more feedback data, faster iteration cycles, and stronger network effects for features that depend on social context — exactly the kind of personalized, memory-aware behavior that agentic assistants need to get right.
What faster agentic adoption means for creators
For people who generate images and build characters with AI, the pace of Muse's growth is a signal worth tracking even if Muse itself isn't a generation tool. Rapid mainstream adoption of agentic assistants accelerates the normalization of AI that acts on your behalf — scheduling, sourcing references, managing project files — rather than just responding to prompts.
That shift has workflow implications. As agentic tools become the default interface for millions of users, the platforms and models that integrate cleanly into those agents gain a distribution edge. Creators who already use AI generation tools through API-connected workflows are positioned to benefit first; those still working in isolated, single-step tools may find the gap between their setup and the mainstream widening.
The competitive dynamics here also echo the broader race described in the AI industry's current incentive structure — labs and platforms are shipping fast because user acquisition at this stage compounds into data and influence that's hard to recover later.
Muse's agentic design also raises the practical question of what happens when a widely adopted assistant has persistent access to your files and communications. The transparency problem noted at launch — Muse's inability to clearly explain its own behavior — hasn't been publicly resolved. For creators who store prompt libraries, reference images, or client work locally, that's a concrete reason to read the permissions carefully before connecting the app.
For now, the Appfigures numbers confirm that the agentic AI moment isn't approaching — it's already here, moving faster than the ChatGPT launch that most people use as their reference point. If you're building or refining a creative workflow, the AI tools and models at Charmloop increasingly need to be evaluated not just for output quality but for how well they fit into an agent-connected pipeline.
