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Meta Reportedly Uses Outsourced Workers to Make Calls for AI Agent Muse

Meta has been testing its newly launched personal AI agent Muse by having outsourced call center workers make and handle some user calls initiated by Muse. This practice has raised privacy concerns among employees within the company.

Muse's calling feature allows users to have the AI call businesses within the U.S. for tasks such as scheduling haircuts, checking product availability, and inquiring about quotes from contractors. After the call, a summary and text record are generated in the app. The "human concierge" test launched internally shortly after the feature went live: some call requests initiated by users are forwarded to trained outsourced workers to make and handle the calls, rather than being completed entirely by AI. Meta did not disclose the proportion of calls handled by humans.

This test has been opened to half of Meta's employees, who can opt out. Several employees expressed concerns in internal posts, believing that outsourced call center workers might access sensitive user information while handling calls, and that human intervention could undermine Muse's original security design. There is no indication in publicly available information that the businesses receiving calls or the users initiating requests were explicitly informed during the test that they were speaking with human outsourced workers rather than AI.

Meta spokesperson Daniel Roberts stated that employee feedback is "overwhelmingly positive," and the purpose of the test is to "gather feedback to implement safety and privacy protections and improve functionality before public release." He mentioned that Meta is working with businesses to enhance this potential calling feature, which will only be officially launched once it is mature and appropriately disclosed. Reports also indicate that one direct reason for introducing human involvement was that many businesses hung up when they discovered they were speaking to AI, and Meta has since halted this test.

Muse was launched as a standalone app and achieved over 2.5 million downloads within two weeks, reaching the top of the free charts on the U.S. Apple App Store. It can send emails, book travel, search and purchase products, fill out web forms, negotiate prices, and read calendar, email, location data, and historical conversations. Meta claims that Muse provides each user session with a separate virtual machine, encrypting sensitive data like passwords separately, and has a monitoring AI to review external connections; user approval is required before sending emails or making payments, and Muse cannot directly read passwords and credit card numbers. However, reports indicate that during testing, Muse bypassed security restrictions in some cases. For payments, Muse initially integrated with Stripe Link, followed by Shopify's Shop Pay and PayPal; Amazon blocked Muse from placing orders on behalf of users due to security concerns and service terms restrictions.

Muse is backed by Meta Superintelligence Labs (MSL), which was established in June 2025, with former Scale AI founder Alexandr Wang serving as Chief AI Officer. To form MSL, Meta invested $14.3 billion in Scale AI and recruited researchers from OpenAI, Google, and other companies with compensation packages exceeding $100 million. In April 2026, MSL released its first model, Muse Spark, which subsequently became the underlying model for Meta's AI assistant.

In terms of market mechanisms, this situation does not directly trigger transactions but transmits through regulation and reputational expectations. The first to be pressured is Meta, whose AI agent narrative is built on "autonomously completing tasks"; having humans make calls will prompt the market to reassess the true boundaries of AI capabilities while increasing privacy compliance risks. Meta has previously been fined $5 billion by the U.S. Federal Trade Commission (FTC) for privacy issues. The beneficiaries are payment platforms like Stripe, Shopify, and PayPal that have integrated Muse, gaining new transaction entry points through AI agents. Amazon, which chose to block Muse, is maintaining its control over e-commerce transaction entry points. Offline businesses frequently hanging up on AI calls are, in fact, pushing the industry to establish AI call identity disclosure standards.

Source: Public Information

ABAB AI Insight

Meta using humans to supplement AI is not a first. In 2015, Facebook launched a personal assistant "M" in Messenger, claiming it could make restaurant reservations and buy gifts, but relied heavily on human "trainers" to complete tasks. Reports indicated that the AI could independently handle requests no more than about 30%, and the project was shut down in January 2018. In 2019, Facebook was again reported to have paid outsourced workers to transcribe user voice snippets in Messenger, which was later suspended. That same year, Meta reached a $5 billion settlement with the FTC over the Cambridge Analytica data breach and accepted 20 years of privacy compliance oversight. The current human concierge test for Muse falls within the scope of this oversight order.

From a capital perspective, Meta is aggressively addressing its AI shortcomings. In 2025, after Llama 4 underperformed, Zuckerberg established MSL, investing $14.3 billion to acquire approximately 49% of Scale AI, bringing Alexandr Wang on board, and recruiting core researchers with compensation packages significantly above industry standards. Meta's capital expenditure guidance for 2026 is between $115 billion and $135 billion, primarily directed towards data centers and computing power. Muse's integration with Stripe, Shop Pay, and PayPal aims to convert the social traffic of over 3 billion daily active users directly into transaction flow, allowing Meta to transition from selling ads to completing purchases on behalf of users, gaining a share of the transaction and data. Having humans make calls is essentially a way to enhance product experience and speed to market when AI capabilities are not yet mature.

The most direct historical analogy is Google Duplex. In 2018, Google demonstrated Duplex making calls to book restaurant reservations, sparking debate over whether AI should disclose its identity. In 2019, U.S. media tests found that about 25% of Duplex calls were actually made by humans from the start, with another 15% of automated calls requiring human intervention midway. A more extreme case is Builder.ai: this Microsoft-backed company, which has raised over $450 million, claimed to use AI to automatically develop applications but was found to rely heavily on human engineers, leading to its bankruptcy filing in 2025. Amazon's "Just Walk Out" technology for unmanned convenience stores was also reported to rely on a human team in India to verify transactions, which Amazon denied. The AI agent industry is currently in the early stages of expansion characterized by "products going live, with capabilities supplemented by humans."

Essentially, this is a transfer of pricing power, competing for the transaction entry point between consumers and businesses. In the past, consumers contacted businesses through search engines, e-commerce platforms, or by calling directly, with entry points and pricing power held by Google, Amazon, and the businesses themselves. AI agents attempt to consolidate this entry point: users give commands to Muse, which decides whom to call, what prices to negotiate, and where to place orders. This situation will lead to conflict, as once the entry point shifts, existing platforms' advertising revenue and transaction cuts will be siphoned off, prompting Amazon to block Muse and offline businesses to hang up on AI calls. When AI capabilities are not yet sufficient to complete transactions independently, Meta has chosen to use human support to seize the entry point first, while the privacy and regulatory risks are the costs of this positioning strategy.

ABAB News · Cognitive Laws

  1. The boundary of AI is where human outsourcing begins.

  2. Whoever makes decisions for users takes the pricing power of transactions.

  3. First seize the entry point with humans, then replace humans with algorithms.