From Muse to the Next Battle Among Tech Giants
Written By: Jenny Shi
Imagine telling an AI that you want to find a summer internship. Instead of simply suggesting job boards or offering career advice, it helps you organize your search, identify relevant opportunities, plan application deadlines, and remind you what to do next.
You no longer need to switch constantly between websites or explain your goals from scratch every time. The AI is no longer just answering questions. It is beginning to work toward a goal on your behalf.
This is the promise of AI agents.
In September 2026, Meta introduced Muse, a personal AI agent designed to handle multi-step tasks through a browser and connected applications. On October 12, Meta announced two additional models, Muse Wonder and Muse Wonder Full, further advancing its efforts in personal and professional AI tasks.
Muse matters not simply because Meta has launched another AI product, but because it points to a broader shift in the technology industry: as AI begins to act on users’ behalf, who will control the next gateway between people and the digital world?
The answer may depend on more than who builds the most powerful model.
1. From Answering Questions to Getting Things Done
Over the past few years, generative AI has made it easier to access information and create content.
Need help drafting an email, understanding a difficult concept, summarizing a research paper, or writing code? Ask an AI assistant, and it can often produce a useful result. Yet even when the task is complex, users generally remain responsible for carrying out the next steps.
AI agents aim to change this relationship.
Consider planning a trip. With a conventional chatbot, you might ask for destination recommendations, search for flights separately, compare hotels, check your schedule, and make the necessary bookings yourself. An AI agent, by contrast, aims to coordinate these steps and, with appropriate authorization, carry out parts of the process for you.
Meta’s Muse is designed around this idea. According to Meta, Muse operates within a dedicated cloud-based virtual machine, uses its own browser, connects to selected applications, and can continue working on multi-step tasks in the background. Its design also includes activity visibility and confirmation for certain sensitive actions, such as sending emails or making purchases.
The potential value of AI is therefore expanding from providing an answer to helping achieve an outcome.
Muse is not the only product pursuing this direction, nor does its launch mean that complex tasks can already be automated reliably in every situation. Rather, it represents a broader shift in how AI products are being developed.
When AI moves beyond generating information and begins taking action, the nature of competition in the technology industry changes as well.
2. Why Is Meta Building Muse?
To understand the strategic significance of Muse, we need to look beyond its features and ask why Meta wants users to adopt this kind of product.
2.1 From Competing for Attention to Competing for the User’s Next Action
One of Meta’s longstanding strengths is its ability to capture user attention.
Facebook connects people through social interactions, Instagram shapes how users consume content, and WhatsApp supports everyday communication. Together, Meta’s products provide numerous touchpoints throughout users’ digital lives.
AI agents introduce a different possibility.
Imagine that, instead of opening several websites to plan a trip, find a service, or organize a project, you could simply describe your goal to an AI. The agent would search for information, compare alternatives, coordinate the steps, and carry out authorized actions.
Users might no longer need to visit every service themselves. The AI could become an intermediary between users and the services they rely on.
This represents a potentially important strategic shift.
In the traditional platform economy, companies compete to attract users, capture attention, and encourage engagement. In an agent-driven environment, they may increasingly compete to become the AI users turn to when they want something done.
The next competition may be about more than who keeps users on a platform longest. It may be about who becomes their first point of contact when they need to accomplish a task.
For Meta, Muse offers a potential way to connect AI capabilities with its existing consumer ecosystem. If users grow accustomed to delegating everyday tasks to a particular agent, that agent could become a recurring entry point into the digital world.
However, a large user base does not guarantee success. Meta must still demonstrate that people are willing to entrust more tasks to Muse and that the product can deliver reliable results.
2.2 From Reaching Users to Understanding Them
Another potential advantage of personal AI agents lies in their ability to understand users’ goals and preferences over time.
A conventional chatbot typically focuses on the current conversation. Even when it has memory capabilities, users still need to consider whether it understands the broader context of a task.
A personal agent, by contrast, could become more useful by retaining relevant information across tasks and applying it when needed.
Consider a student looking for an internship. The search may involve academic background, career interests, application deadlines, and class schedules. An AI that can appropriately retain and use this information, with the user’s permission, could move beyond recommending job listings to helping organize applications, track progress, and plan follow-up actions.
Meta’s Muse Wonder models, announced in October, highlight capabilities such as Active Memory and Active Focus. According to Meta, these are designed to help models retain relevant context across conversations and tasks, and to persist through difficulties by trying alternative approaches and recovering from errors.
This helps explain why the competition is not simply about which AI generates the best answer. It is also about which AI can understand a user’s ongoing needs and translate that understanding into useful action.
But there is a trade-off. The more an AI understands about a person, the more personalized its assistance can become. At the same time, the more personal information it handles, the greater the need for effective privacy protections and responsible data use.
Understanding the user can therefore become both a competitive advantage and a trust challenge.
2.3 From a Standalone Product to an Integrated AI Ecosystem
If an AI agent is the product users interact with, the underlying model helps determine what that product can accomplish.
A useful agent needs more than natural language understanding. It must also be able to plan tasks, use tools, recover from errors, and recognize when it cannot reliably complete an action.
Meta’s October 12 announcement of Muse Wonder and Muse Wonder Full represents another step in its efforts to strengthen the models behind its agent strategy. Meta has positioned these models for personal and professional tasks and plans to integrate Muse Wonder into Muse.
Strategically, this suggests an effort to connect foundational AI capabilities with consumer-facing products.
There are potential benefits to this approach. Improvements in underlying models can enhance the user experience, while real-world use cases can help identify what users need. Yet model development, computing infrastructure, product engineering, and safety measures all require sustained investment.
Launching an AI agent is not the same as building a competitive AI ecosystem. The real challenge is to make the underlying technology, user experience, and execution capabilities work together consistently.
3. What Will Tech Giants Actually Compete For?
Looking at Muse provides a useful lens through which to examine four interconnected dimensions of the broader AI competition.
3.1 Model Capability: Can the AI Get the Task Right?
Model capability remains fundamental.
Understanding complex instructions, processing different types of information, planning multiple steps, and adjusting when something goes wrong all affect an agent’s performance.
However, a powerful model does not automatically produce successful outcomes. What users ultimately care about is whether the AI can solve their problems accurately and consistently, not merely how well it performs on a benchmark.
3.2 User Access: Why Will People Choose Your Agent?
Meta has an established consumer ecosystem, but other technology companies have their own products, distribution channels, and user relationships.
As more services become accessible through AI, users’ choices may depend on convenience, personalization, reliability, and how naturally an agent fits into their everyday lives.
Existing traffic can help a product gain initial adoption, but it does not guarantee long-term loyalty.
3.3 Execution Ecosystems: Can the AI Actually Get Things Done?
An agent’s value depends not only on the model itself but also on its ability to work with external tools and services.
If an agent can offer recommendations but cannot reliably handle the next steps, users must still do much of the work themselves. If it can coordinate different tools and complete multi-step tasks within authorized boundaries, it could fundamentally change how people interact with digital services.
Competition may therefore extend beyond individual models to the broader infrastructure of tool integration, service compatibility, and task execution.
3.4 Trust: Will Users Let the AI Act on Their Behalf?
This may be one of the most important differences between AI agents and conventional chatbots.
When AI generates text, an error may simply require the user to check or revise an answer. When AI can operate applications, send messages, or make purchases, mistakes can have more direct consequences.
Meta has described several safeguards for Muse, including an isolated virtual machine, permission controls, and confirmation for certain sensitive actions. These measures reflect a broader reality: security and privacy are not optional add-ons for AI agents. They are integral to how such products must operate.
Still, the existence of safeguards does not mean that every risk has been eliminated. Users must consider whether an agent can correctly interpret instructions, resist malicious inputs, identify mistakes, and prevent unauthorized actions.
In the future, trust may depend not only on how much an agent can do, but also on whether it can explain what it has done, recognize when it needs approval, and keep users in control.
4. Will the Future of AI Agents Belong to Tech Giants?
Meta has several resources that could help it compete: an established consumer ecosystem, a large user base, and the capacity to invest in models and infrastructure.
But these advantages do not guarantee that it will win.
First, user habits are difficult to change. People may be comfortable using a platform for social interaction or messaging without necessarily wanting to entrust more personal tasks to its AI.
Second, the business model remains an important question. Agents that execute complex tasks require resources for model inference, tool use, and cloud computing. Companies must find sustainable ways to cover these costs while delivering enough value for users to keep coming back.
Finally, there is the question of trust.
As AI gains access to personal schedules, work documents, communications, and potentially payment-related actions, privacy, permission management, and accountability become central to whether these products can gain widespread adoption.
Tech giants are also not the only potential winners. Smaller companies focused on specific industries or tasks may compete through specialized capabilities, clearer use cases, or better user experiences.
The future of AI agents therefore may not be a simple winner-takes-all contest among a handful of technology giants. Instead, the industry could develop into a layered ecosystem: some companies provide foundational models, others build user-facing platforms, and still others specialize in executing tasks in particular domains.
The more important question is how these players will divide the value created between users, services, and technology.
Conclusion: Who Will Become the AI People Trust?
Muse illustrates how the central questions in AI are beginning to evolve.
Until recently, much of the discussion focused on whether AI could answer difficult questions, generate high-quality content, or achieve impressive results on benchmarks.
As AI agents develop, we must also ask different questions. Can they understand real-world needs? Can they make progress toward a goal? Can they coordinate different services? And can they do all this while allowing users to remain in control?
This does not make model capability any less important. On the contrary, models, products, ecosystems, and safety will become increasingly interconnected.
For Meta, Muse is an attempt to compete for the personal AI entry point. For the broader industry, it offers a window into how the next phase of competition might unfold.
The next battle may not be won simply by the company with the smartest AI, but by the company that makes AI useful enough to become part of everyday life—and trustworthy enough for people to let it act on their behalf.
References
Meta, Introducing Muse: The World’s First Personal AI Agent Built for Everyone, September 2026.
Meta, Introducing Muse Wonder and Muse Wonder Full, October 12, 2026.
