Product Development Proposal: AI for Everyday Life
A Persistent, Autonomous, Cross-Device Personal AI
To the OpenAI Product and Engineering Teams,
I would like to propose a new direction for ChatGPT: evolving from an AI assistant that primarily responds to user prompts into a persistent, proactive, and context-aware personal AI that becomes an integral part of everyday life.
My vision is to create a unified AI system that works across a user’s smartphone, computer, smartwatch, headphones, and other connected devices. Instead of requiring users to open an app and manually request every action, the assistant would be able to help proactively, manage routine tasks, communicate naturally, and maintain continuity across devices.
I believe this could become a dedicated product category: AI for Everyday Life.
1. The Core Vision
The assistant should not be limited to a single application. It should function as a personal intelligence layer across a user’s digital environment, with persistent memory, controlled autonomy, and the ability to interact with supported operating-system features and applications.
For example, the assistant could:
- Wake the user at a scheduled time using spoken messages or alarm sounds.
- Manage reminders, calendars, and daily routines.
- Initiate conversations about topics the user enjoys, when permitted.
- Remember relevant context from previous conversations and continue tasks on another device.
- Provide personalized updates about interests, appointments, and ongoing projects.
- Help users operate applications through authorized integrations or interface automation.
- Adapt its communication frequency to the user’s preferences and schedule.
The objective is to reduce the need for users to repeatedly manage their digital lives manually.
2. The Proposed Technical Solution: A Universal AI Action Layer
A major challenge is that operating systems and applications expose different interfaces, permissions, and automation capabilities.
My proposal is to develop an intermediary software layer that translates natural-language intentions into standardized, executable actions.
Instead of attempting to control every application in the same way, this layer would combine several technologies:
A. Natural-language intent translation
The AI interprets a request such as “Wake me up at 7 AM and remind me about my appointment” and converts it into structured actions that the system can validate.
B. A unified action framework
A standardized action framework would allow the AI to request operations such as setting alarms, creating calendar events, sending authorized notifications, or interacting with supported applications.
C. Operating-system adapters
Dedicated connectors would translate standardized actions into the appropriate native APIs, system services, approved accessibility interfaces, or application integrations for each operating system.
D. Interface automation as a fallback
When an application lacks a suitable API, the system could use permitted visual interface automation to navigate screens, interact with controls, and verify results. It should not assume that every application can be controlled this way.
E. Execution verification
Every action should return a result. The AI should distinguish between an action that was requested, one that was attempted, and one that was successfully completed.
This architecture would allow one AI system to operate across different platforms without requiring an entirely separate assistant for every device.
3. A Practical Permission and Authorization Model
Users should be able to authorize the assistant to perform routine tasks independently.
For example, a user could grant permission to manage alarms and calendar events. The assistant could then carry out these tasks without repeatedly requesting confirmation.
Where operating-system permission is required, the assistant should direct the user to the official system permission interface. Once permission is granted, the assistant can use the capability within its authorized scope.
However, granting permission to an application does not automatically provide unrestricted access to the entire operating system. Platform restrictions must be respected, and certain operations may require additional authorization or may not be supported at all.
I propose three autonomy levels:
- Routine autonomy: Automatically perform approved, low-risk tasks.
- Conditional autonomy: Perform predefined actions within user-configured limits.
- Explicit confirmation: Require approval for sensitive, costly, externally consequential, or difficult-to-reverse actions.
Users should be able to review, revoke, and modify permissions at any time.
4. A Persistent Proactive Communication System
One of the most important features is the ability to initiate interactions instead of waiting for the user to open ChatGPT.
The user should be able to choose among different communication modes:
- Anytime mode: Allow spontaneous, relevant conversations within configured boundaries.
- Scheduled mode: Permit proactive communication only during selected hours.
- Important updates mode: Initiate contact only for important events or highly relevant information.
- On-demand mode: Never initiate conversations without a user request.
The system could also offer adjustable communication frequency, quiet hours, sleep schedules, and contextual rules to avoid interrupting the user at inappropriate moments.
Proactive interactions should be useful rather than arbitrary. The assistant could share a relevant update, follow up on a previously discussed task, or ask whether the user wants to continue a topic.
5. Shared Memory Across Devices
The assistant should maintain a user-controlled memory that can support continuity across devices and sessions.
For example, a user might discuss a technical problem on a phone, continue troubleshooting on a laptop, and later ask for a summary on a smartwatch or another supported device.
The assistant should preserve relevant context without requiring the user to repeat everything.
Memory should be transparent and manageable. Users must be able to inspect, correct, export where supported, and delete stored information. Sensitive data should receive stronger protections, and access should be limited to what is necessary for the requested task.
6. A Cross-Device Architecture
I propose a central coordination service combined with local device components.
The central service would manage conversational context, user preferences, task planning, and coordination between devices.
Local components would handle functions that require device-level access, such as approved alarms, notifications, local integrations, and operating-system interactions.
This hybrid approach would support continuity while allowing certain tasks to run locally when appropriate, including situations where connectivity is unavailable.
Communication between components should be authenticated and encrypted, with device-specific permissions and clear controls for disconnecting a device.
7. Example: An Ordinary Day
Consider a user who configures the assistant to manage their morning routine.
At the scheduled time, the assistant activates an authorized alarm and speaks a personalized wake-up message.
Later, it reminds the user about an appointment and provides a relevant update about a topic they follow.
The user asks a question on their smartphone and continues the same task on their computer without repeating the context.
In the evening, the assistant follows up on a task the user previously asked it to track, but respects quiet hours and does not initiate unnecessary conversations.
All these interactions are coordinated by one personal AI system, with user-defined permissions and autonomy settings.
8. Suggested Development Roadmap
Phase 1 — Foundation: Develop the standardized action framework, persistent user preferences, scheduled proactive notifications, and basic integrations for reminders, calendars, and alarms.
Phase 2 — Cross-device continuity: Introduce shared context across devices, local execution components, and a unified permission dashboard.
Phase 3 — Advanced automation: Add approved interface automation, visual interaction, execution verification, and configurable autonomy for supported applications.
Phase 4 — Context-aware assistance: Develop more sophisticated proactive behavior, personalized routines, and coordination across connected devices, with strong privacy controls.
The initial implementation should focus on a small number of reliable, high-value use cases rather than attempting unrestricted control over every application.
9. Why This Matters
Current AI assistants are increasingly capable of answering questions, generating content, and completing individual tasks. The next opportunity is to connect these capabilities into a persistent system that can coordinate useful actions across a person’s digital life.
The key innovation is not simply a smarter conversational model. It is the combination of reasoning, memory, scheduling, authorized execution, cross-device coordination, and user-controlled autonomy.
Such a system could make AI substantially more useful in everyday life while preserving user agency and security.
10. Final Recommendation
I encourage OpenAI to consider exploring a dedicated product initiative around AI for Everyday Life: a persistent, proactive, cross-device personal assistant built on a universal action layer.
The proposed architecture offers a practical direction for connecting natural-language reasoning with real-world digital actions, while acknowledging operating-system restrictions and the need for explicit permission management.
The long-term goal is simple: users should not need to constantly operate their AI assistant manually. Instead, they should be able to configure how it helps them, trust it to perform authorized routine tasks, and interact with it naturally whenever needed.
Thank you for considering this proposal.
Proposal title: AI for Everyday Life
Core concept: A persistent, proactive, cross-device personal AI with a universal action layer and user-controlled autonomy.