Analyzing Meta Muse: Anticipated Application Scenarios and AI Monetization Models

Early Download Data of Meta Muse Has Limited Significance for Discussion

On September 9, Meta launched a new artificial intelligence agent (AI Agent) tool called 'Muse,' which has quickly achieved impressive results on U.S. app store charts over the past two weeks. According to data from market research firm Sensor Tower, within its first six days, Muse reached the No. 1 spot on both the U.S. Apple iOS and Google Play free app charts, surpassing one million downloads and outperforming previous product launches. It has also exceeded early mobile performance metrics of ChatGPT, prompting foreign investment firms to raise their target prices for META stock and contributing to a surge in U.S. tech stocks on September 21—this week’s primary positive catalyst.

Regarding these figures and market trends, the author believes we must consider the different temporal and contextual backgrounds. Comparisons of early-stage data are not made on equal footing. During the ChatGPT era, the general public had almost no understanding of LLMs or generative AI; thus, naturally, early performance was modest. The significance of ChatGPT lay in its groundbreaking transformer architecture and large-scale application. In contrast, today’s launch of Muse occurs in an environment where the public is already familiar with AI—and often misuses or overuses it under subsidized token models. Consequently, free version downloads and active user counts accumulate rapidly. This is a classic case of comparing apples to oranges.

The Real Focus: Early User Feedback and Anticipated Use Cases

Muse features cross-platform integration capabilities, connecting via browser to services such as Gmail, Google Calendar, Notion, OpenTable, or financial software, enabling it to handle daily digital tasks through third-party platforms. Beyond the standalone Muse app, it is also deeply integrated into Meta’s WhatsApp.

Based on early user feedback and shared experiences, this center speculates that Muse’s key advantage lies in commercialization potential—specifically, bridging the gap between ad-like engagement and final payment conversion. For example: after consultation and cross-platform price comparison, completing reservations for merchants or services; another similar use case involves loan interest rates, repeatedly calling banks and negotiating lower rates through strategic communication. In the U.S., users have successfully negotiated via email exchanges with banks.

Key Advantages and Operational Benefits:

1. Leveraging Meta’s dominance in social media, data sources include content from hundreds of millions of users’ social accounts (e.g., private Instagram profiles).

2. Expanding revenue beyond the previous 90% reliance on advertising, now incorporating subscription and transaction revenue sharing, thereby demonstrating clear AI monetization to the market.

→ Overall, main applications revolve around everyday scheduling tasks such as booking (restaurants, taxis, services, courses) and repetitive, complex price comparisons. It extends beyond simple Q&A to more comprehensive executive assistant functions. However, AI agents capable of repeatedly triggered single-task execution remain limited in scope—these are still closer to AI assistants than true autonomous agents.

Pricing Structure

Competitor and Supply Chain Vertical Integration Comparison

Currently, the only major competitor to AI assistants like Muse is SpaceX’s (xAI) Grok bot. This remains a blue ocean market, and the business models of these two companies are relatively similar:

1. Both operate on massive existing SNS app user bases with access to unique personal data.

2. Unlike the three major public cloud providers (Google, Microsoft, AWS), they lack fallback infrastructure for AI operations.

3. They consistently lag behind in large model development (compared to OpenAI, Anthropic).

4. They lack mature ASIC hardware, resulting in higher AI operational costs (compared to Google, AWS), thus shifting focus toward AI assistant development.

Extended Discussion and Future Tracking Topics:

Market mindshare among AI apps is becoming critical—the competition between software platforms is emerging as the key battleground. Amazon’s preemptive ban on Muse reveals this trend.

Additionally, mobile-first AI applications continue to lead, indirectly proving that high-precision, broadly applicable ultimate AI agents are difficult to implement in modern society. True AI agents face high technical and hardware setup barriers (e.g., personal workstations), leading companies to opt for simplified, downgraded versions. This trade-off is a significant direction worth tracking in AI development, indicating that edge AI and real-world deployment may not arrive as quickly or smoothly as imagined.

(Written by Fan Zhen-Feng, Analyst at Yung-Cheng Asset Management Division)

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