Meta Muse AI Agent Goes Viral as Agent Products Speed Up Consumer Adoption, Says Orient Securities

Stock News
09/28

According to Zhitong Finance APP, Orient Securities Company Limited (ASX: 03958) released a research report stating that on September 8, Meta Muse launched, positioned as an AI personal agent tool. Within five days of launch, its downloads surpassed ChatGPT, making it the most downloaded free app on the US iOS platform. The bank is bullish on the commercialization and industry iteration opportunities of Work Agent in the LLM track over the long term, with the core logic driven by two key dimensions: upgrades to the model capability foundation and deepening of vertical scenario engineering implementation. Leading model manufacturers have begun to deeply cultivate vertical scenarios, helping Work Agent complete the closed-loop construction from technical prototype to industrial implementation. Current industry competition has moved beyond the shallow stage of general model parameter comparison, shifting to refined implementation competition around vertical task definition, real workflow trajectory accumulation, and dedicated Reward feedback mechanism construction.

Key viewpoints of Orient Securities are as follows:

Meta Muse positioned as an AI agent entry point for ordinary users, with impressive daily active user data

Meta Muse's core functions include cross-network task execution, such as filling out electronic forms and organizing email inboxes, aiming to enable users to manage and schedule powerful digital assistants. It is Meta's AI agent product for ordinary consumers. According to Apptopia data, in the first 12 days after Muse launched in the US and Canadian markets, its iOS downloads reached 1.8 million, higher than ChatGPT's 1.3 million in the same period; US mobile DAU reached 642,000, far higher than ChatGPT's 231,000 in the same period. The bank believes that C-end Agent applications continue to expand their reach, and the value of Agents for ordinary users is deepening.

Driven by both technology and channel, bullish on platform ecosystem manufacturers achieving rapid breakthroughs in Agent scenarios

On the technology side, Muse's product foundation is driven by Meta's Muse Spark series AI models. On the channel side, Meta has significant platform advantages in user traffic diversion. According to Apptopia data, over 95% of Muse users are also Facebook users, and 63% are Instagram users, achieving rapid user acquisition for ordinary users through cross-platform traffic diversion strategies. In terms of business model, Muse offers a free basic version, along with two subscription tiers at $20 per month and $100 per month. The bank believes that Meta's accumulation on the channel side can accelerate the penetration of AI Agent products from early adopters to mass users. The bank is bullish on manufacturers with massive social user bases, relying on large model foundations plus social traffic entry points, to be the first to establish a scalable commercialization path for C-end AI Agents.

Long-range task planning and state memory, stronger tool invocation expected to enhance the complexity of autonomous task execution for C-end Agents

The bank believes that current C-end Agent products are still in the stage of partial intelligence, meaning they have strong autonomous closed-loop capabilities in tasks such as email organization, subscription detection, web search, shopping filtering, and standardized forms; however, when facing cross-application long-chain tasks, ambiguous open-ended requirements, and multi-person coordination, human takeover is still needed. For subsequent products to achieve higher-level intelligence, the bank believes that long-range task planning and state memory capabilities, stronger tool invocation and environmental interaction, and user context accumulation will all help propel C-end Agents from instruction executors to personal intelligent agents capable of autonomously completing complex affairs.

Risk warnings: AI technology iteration falling short of expectations, AI application implementation falling short of expectations, AI commercialization monetization falling short of expectations.

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