Current LLMs are intelligent enough for many tasks but fail because they lack access to complete context—emails, Slack messages, past data. The next step is building products that ingest this real-world context, making it available for the model to act upon.

Even if your strategy is a ubiquitous AI layer, building your own applications (like an email client) is essential. These dedicated "surfaces" allow you to fully express your vision for an AI-native experience, which is constrained when only building on top of others' products.

The emergence of personal AI assistants that can be integrated with private data (email, Slack) and execute tasks (send emails, build CRMs) represents a new paradigm. This moves AI from a passive research tool to an active, autonomous agent capable of performing complex knowledge work, fundamentally changing productivity.

Your mental model for AI must evolve from "chatbot" to "agent manager." Systematically test specialized agents against base LLMs on standardized tasks to learn what can be reliably delegated versus what requires oversight. This is a critical skill for managing future workflows.

Tools like ChatGPT are AI models you converse with, requiring constant input for each step. Autonomous agents like OpenClaw represent a fundamental shift where users delegate outcomes, not just tasks. The AI works autonomously to manage calendars, send emails, or check-in for flights without step-by-step human guidance.

The primary barrier to widespread AI adoption is not the power of the models, but the difficulty of embedding them into users' existing habits. Meeting users where they already are—like their email inbox—is more effective than forcing them to adopt new applications or behaviors.

User workflows rarely exist in a single application; they span tools like Slack, calendars, and documents. A truly helpful AI must operate across these tools, creating a unified "desired path" that reflects how people actually work, rather than being confined by app boundaries.

By summarizing emails and suggesting 'to-dos', Google is embedding agentic AI into a daily habit for over two billion users. This strategy serves as a massive, low-friction entry point to familiarize consumers with AI assistants that perform tasks on their behalf, potentially driving mass adoption for its Gemini ecosystem.

The next major leap for AI is its ability to connect disparate apps and data sources (email, calendar, location) to take autonomous actions. This will move AI from a Q&A tool to a proactive agent that seamlessly manages complex workflows.