Mark Zuckerberg’s recent announcement regarding Meta’s new “Muse” AI agent signals what might be the most aggressive move yet to bring autonomous, money-making technology directly to the individual [04:16].

In a recent comprehensive interview, Zuckerberg laid out the vision for Muse, which represents a significant departure from the standard “chatbot” model we’ve grown accustomed to.

From Chatbots to 24/7 Virtual Machines Instead of sending a prompt and waiting for a text response, Muse acts as a dedicated virtual machine operating in the background [23:25]. Users give the agent projects or goals, and it relentlessly works on their behalf 24/7 until the task is achieved [24:03]. It even studies and consolidates memory overnight [24:13]. The system is designed to be proactive, suggesting new ways it can expand on projects or run daily household and business tasks.

The Economic Engine: Making Money for Users Perhaps the most disruptive aspect of Muse is Meta’s business model for it. Zuckerberg stated directly that he believes this agent will make and save people money [25:01]. Meta is providing an enormous free tier—offering 100 million tokens a week for free, alongside a virtual machine, to ensure the technology is widely distributed [25:20].

For users running small businesses or handling commerce, the agent can connect to Meta’s ad systems and automatically optimize digital marketing [26:59]. Meta expects the agent to generate so much financial value for its users that the company’s eventual revenue will simply come from taking a tiny cut of transactions, powered by a partnership with Stripe [26:05].

Unprecedented Privacy: The Confidential VM To allow an AI agent to run a business, manage emails, or handle personal schedules, it requires deep access to sensitive information. Recognizing this, Meta recruited Moxie Marlinspike, the founder of the highly secure messaging app Signal, to architect a “Confidential VM” [32:20].

Drawing on Meta’s experience securing WhatsApp, the Muse infrastructure is designed so that not even Meta itself can see the data inside your agent’s virtual machine [32:28]. The system strictly utilizes a principle of “least privilege,” requiring human-in-the-loop approvals via “Sentinel agents” before it can send sensitive information or execute payments [35:32].

What’s Next: The “Watermelon” Model Meta is powering this ecosystem with rapidly advancing underlying models. Following their internal “Avocado” training run, Zuckerberg confirmed that an even larger, more capable pre-trained model codenamed “Watermelon” is shipping soon [48:29]. As these models compound in intelligence, the barrier to entry for launching and automating a global business is about to hit zero.