Personal AI Assistants: How to Get Started & How to Invest

Personal AI assistants are moving beyond answering questions to carrying out everyday tasks through several steps. Meta Platforms’ (META) Muse agent is one example that has taken the world by storm over the past week. It works in its own virtual environment, with its own “computer,” “browser,” and “memory” customized to each user.

It can perform tasks, suggest ideas or goals, and is otherwise designed to align with and serve the user, including making phone calls for doctor’s appointments, reservations, and many more use cases.

Much of the work happens behind the scenes. Cloud computing supplies model capacity, software connects services, and security systems govern what an assistant may do. Wearables can add timely context through sensors and low-power chips. These layers make personal AI a useful way to understand the wider AI value chain, while the person sets the goal and reviews important decisions.

• Personal agents can take on errands that have sat on a to-do list for months. Start with a real goal, give the assistant useful context, and let it handle the research and follow-up.
• Meta’s new Muse agent is one consumer-facing path. Projects such as OpenClaw, Hermes Agent, and Instinct show other ways an assistant can work across tools and messages.
• We expect wearables with sensors and low-power processors, hands-free control beyond taps, keyboards, and mice, and connected-health devices to see massive adoption as the other half of this personal AI cycle.

A chatbot can answer a question. A personal agent can carry a goal through several steps, use tools, and return with a finished result or a decision for you. Instinct, which works through familiar messaging channels, offers another glimpse of the experience. In an anonymous, self-reported example, a user sent a voice note after a paper parking ticket blew away. The assistant checked the county portal until the citation appeared, paid it with a saved card, and returned the receipt. A single request became a completed errand.

Here is what personal AI has looked like in my own life. I asked an agent to look for unclaimed funds for me and family members; it found quite a bit, across several states! It tracked which credit card perks I had used and matched the remaining Chase Reserve benefits to upcoming trips. It found a physical therapist who accepted my insurance, emailed their offices about when I could start, and handled the scheduling back-and-forth.

Imagine scrolling through useful actions your AI agent can take on your behalf instead of getting pulled into addictive feeds or searching across a dozen websites. It might find a neighborhood event that fits an open weekend and your goal of getting the kids outdoors more. It could also gather tax forms and other documents you’ve been putting off, or help with urgent tasks that are easy to delay.

To get started, choose a task you would be glad to cross off. Share the context the assistant needs, such as a trip, email thread, account benefit, bill, or preference. If supported, connect Gmail or your calendar so it can keep up with relevant updates. For example:

Look at my upcoming trips and the benefits on my credit cards. Find the perks I can still use, match them to each trip, and take care of any follow-up needed to claim them.

An agent can also help with paperwork, from government forms and finding qualified healthcare that accepts your insurance to reimbursements and claim-denial negotiations. It also has “tracks” of things it can recommend to explore to utilize it in various ways (e.g. personal AI blueprints). Power to the people!

For more ideas, browse the Instinct use-case gallery and the Use Cases for Agents personal-use directory.

When an assistant can navigate the web and handle the shopping steps for a person, it also changes the very nature of product discovery and the customer relationship. Not every platform welcomes that shift. Amazon.com (AMZN) blocked Muse from shopping on its site. According to Axios, Amazon said it had not authorized Muse to access customer accounts, scrape data, or process transactions, and raised concerns about security and user experience.

Meanwhile, I personally have my Muse agent shopping across Facebook Marketplace, eBay, Poshmark, Craigslist, and Grailed all at once for numerous items on my wish list, ranging from clothing to external monitors.

Shopify (SHOP) is taking a more open route and announced a partnership with Meta the same day. Its agentic storefronts let eligible merchants make products available in AI shopping channels, including Meta, while merchants retain the customer relationship after a sale. These approaches show how personal agents may reshape commerce itself as much as the interface. For a deeper look at that business-model angle, see our companion article on personal AI.

The ease of use depends on work that customers rarely see. Meta describes a separate permission system for Muse that evaluates connector actions and network access. The user should have clear approvals and a record of what happened; product teams and security providers build and operate the systems behind those controls.

This is why CrowdStrike (CRWD), Palo Alto Networks (PANW), and Datadog (DDOG) belong in the discussion of agent identity, security, and observability. Their current revenue spans much more than personal agents, but the need to govern software that acts on a user’s behalf is growing.

Muse also puts Meta’s larger investment cycle into a consumer product. Meta expects 2026 capital expenditures of $130 billion to $145 billion, including finance-lease principal payments. That guidance covers its wider AI and core-business infrastructure. Meta has also identified technical talent, particularly for AI, as a major driver of expense growth. Its Superintelligence Labs says it rebuilt the AI stack that produced Muse Spark.

The silicon strategy has a named partner. Meta and Broadcom (AVGO) expanded their partnership to co-develop multiple generations of Meta’s custom MTIA chips for AI inference and recommendations. Broadcom is working across chip design, advanced packaging, and Ethernet networking. Meta says the program will support AI across its apps and services.

Together, models, compute, talent, and distribution across apps and devices are taking shape as Meta 2.0. Muse gives consumers an early view of how those investments can show up in everyday life. Sustained use for meaningful tasks will show how much of that opportunity materializes.

Personal AI can work in the cloud and on a device at the same time. Cloud systems provide model inference, search, data storage, and connections to services. Cameras, microphones, and health sensors gather context close to the user; processing some signals on the device can reduce delay and avoid sending every raw input to the cloud. The most useful experience can draw on both layers.

This past week, the Q3 2026 rebalance saw Ambiq Micro (AMBQ) added to the ROBO Global Artificial Intelligence Index (THNQ). Ambiq is a semiconductor company focused on ultra-low-power chips for edge AI and battery-powered devices. Its SPOT platform supports local processing while reducing power use.

Everyday sleep tracking offers a more familiar example of health data from a wearable. Ambiq’s sleepKIT is designed for smartwatches and fitness bands to detect sleep and classify light, deep, and REM stages using on-device models on its low-power Apollo chips. That illustrates how edge processing can turn routine signals into useful context while limiting power demands. A future personal assistant could help someone review sleep patterns over time, though Ambiq’s product information does not establish an integration with a personal-agent service.

Rapid response is another compelling direction, especially for older adults. An existing smartwatch fall-detection feature can call emergency services after a detected hard fall if the wearer remains immobile, share its location, and send Medical ID details when that option has been enabled. A future wearable-linked assistant might help request an ambulance and, using permissions set in advance, relay relevant conditions, location, and selected sensor readings to responders or family contacts.

That richer agent workflow remains prospective. The need is clear: more than one in four U.S. adults age 65 or older falls each year. We expect rapid response and “aging in place” to become major drivers of personal AI adoption as useful services connect with dependable sensing. Perhaps most importantly, personal AI will level the playing field so people who have felt “out-paced” by newer technology now have a technology partner that can help them overcome tech-illiteracy (and hopefully, avoid scams!).

We expect low-power sensing, local inference, and cloud-based assistance to support a widening set of products, from health monitors to glasses and other wearables. Ambiq reported second-quarter 2026 net sales growth of approximately 90% year over year, citing demand for edge AI solutions. Broadcom also joined THNQ at the Q3 2026 rebalance. The two companies play different roles in the personal AI value chain, from data centers to devices.

The ROBO Global Artificial Intelligence Index (THNQ) tracks companies across the global AI value chain, from the semiconductors behind data-center inference and edge devices to the cloud, software, and security companies that let agents connect, act, and stay governed. That breadth reflects the several sources of demand behind a single personal agent, rather than any one product or platform. Recent additions such as Ambiq and Broadcom show how the index evolves as new layers of the value chain mature.

We are still in the early innings of personal AI. We expect use cases to multiply as agents become easier to use, devices contribute timely context, and more services allow assistants to act. The most compelling outcome is practical: people around the world gaining the ability to finish tasks that once demanded too much time, technical know-how, or persistence.

For more news, information, and analysis, visit the Artificial Intelligence Content Hub.

THNQ is the underlying index for the ROBO Global Artificial Intelligence ETF (THNQ) and the L&G Artificial Intelligence UCITS ETF (AIAI.LN).

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