Treating AI coding tools like an asynchronous junior engineer, rather than a synchronous pair programmer, sets correct expectations. This allows users to delegate tasks, go to meetings, and check in later, enabling true multi-threading of work without the need to babysit the tool.

AI's impact on coding is unfolding in stages. Phase 1 was autocomplete (Copilot). We're now in Phase 2, defined by interactive agents where developers orchestrate tasks with prompts. Phase 3 will be true automation, where agents independently handle complete, albeit simpler, development workflows without direct human guidance.

The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.

Structure your development workflow to leverage the AI agent as a parallel processor. While you focus on a hands-on coding task in the main editor window, delegate a separate, non-blocking task (like scaffolding a new route) to the agent in a side panel, allowing it to "cook in the background."

Because AI agents operate autonomously, developers can now code collaboratively while on calls. They can brainstorm, kick off a feature build, and have it ready for production by the end of the meeting, transforming coding from a solo, heads-down activity to a social one.

Top-performing engineering teams are evolving from hands-on coding to a managerial role. Their primary job is to define tasks, kick off multiple AI agents in parallel, review plans, and approve the final output, rather than implementing the details themselves.

Instead of focusing on foundational models, software engineers should target the creation of AI "agents." These are automated workflows designed to handle specific, repetitive business chores within departments like customer support, sales, or HR. This is where companies see immediate value and are willing to invest.

Instead of relying on a single, all-purpose coding agent, the most effective workflow involves using different agents for their specific strengths. For example, using the 'Friday' agent for UI tasks, 'Charlie' for code reviews, and 'Claude Code' for research and backend logic.

While chat works for human-AI interaction, the infinite canvas is a superior paradigm for multi-agent and human-AI collaboration. It allows for simultaneous, non-distracting parallel work, asynchronous handoffs, and persistent spatial context—all of which are difficult to achieve in a linear, turn-based chat interface.