Meta’s Muse tops ChatGPT in early iOS downloads
Amazon has blocked Muse from shopping, showing that retailer consent can still constrain personal AI agents.
By Saeed Ezzati8 min read
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Meta’s Muse has opened with a stronger early mobile signal than ChatGPT, but the comparison comes with important limits. App-intelligence firm Apptopia estimates that Muse reached 1.8 million iOS downloads in the United States and Canada during its first 12 days, versus 1.3 million for ChatGPT over the comparable period. Apptopia also estimated 642,000 daily active mobile users for Muse in the U.S., compared with 231,000 for ChatGPT at the equivalent point. Muse’s estimated active iOS audience was 359,000. Those are third-party estimates, not figures Meta has confirmed. The comparison is directional, not a global verdict. ChatGPT launched worldwide but initially only on iOS. Muse launched on both iOS and Android, but only in the U.S. and Canada. Apptopia isolated iOS downloads in those two countries to create a more comparable slice, while the launch strategies themselves remained different. Muse also recorded an estimated 2.8 million global installs in its first 12 days. The more consequential distinction is product design. Meta presents Muse as an agent that can send email, book travel, and fill out forms, while seeking approval before sensitive actions. It runs in a dedicated secure virtual machine, a cloud environment intended to contain the agent and connected-service data. That makes sustained trust more important than an initial install: users are being asked to connect more of their digital lives. The early audience also appears closely tied to Meta’s existing reach. More than 95 percent of estimated Muse users also use Facebook, and 63 percent use Instagram. That may help explain the launch, but not whether people return. The next meaningful metric is sustained active use—and whether users are comfortable delegating consequential tasks. That question of trust and delegation also shapes OpenAI’s expanded Academy. Its new role-based paths serve knowledge workers, developers, leaders, educators, and college students. The courses move from better prompting to reusable workflows and agent delegation, with explicit review checkpoints and human ownership of the final result. Learners can earn a badge after passing an assessment, although OpenAI has not explained what the assessment measures or how employers and schools should interpret it. The practical test is whether training improves judgment on real work, not simply access to an AI tool. The same gap between capability and accountability appears in an MIT Technology Review investigation of border-surveillance towers. It mapped nearly 4,000 migrant-death locations and found more than 1,050 within the advertised range of towers, including more than 110 near modern Anduril towers since 2021. Proximity does not prove a detection or response failure. Terrain, virtual boundaries, incomplete records, and footage typically deleted after 30 days limit what can be concluded. But the investigation found that Customs and Border Protection has not conducted a comprehensive review of deaths near towers—a notable omission as the network expands. And at the hardware layer, researchers are exploring systems where the material itself learns. A Nature Reviews Physics review examines nanowire and nanoparticle networks whose changing physical connections perform computation, potentially filtering data locally in satellites, robots, vehicles, and sensors with limited power or connectivity. The systems have completed real-time speech and image-recognition benchmarks, but remain a research platform, not a deployed replacement for silicon or cloud computing. Across these stories, the practical signal is consistent: watch not just what AI can do, but where humans review it, who owns the outcome, and whether real-world constraints are measured after deployment.
