Once AI coding agents reach a high performance level, objective benchmarks become less important than a developer's subjective experience. Like a warrior choosing a sword, the best tool is often the one that has the right "feel," writes code in a preferred style, and integrates seamlessly into a human workflow.

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.

The terminology for AI tools (agent, co-pilot, engineer) is not just branding; it shapes user expectations. An "engineer" implies autonomous, asynchronous problem-solving, distinct from a "co-pilot" that assists or an "agent" that performs single-shot tasks. This positioning is critical for user adoption.

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.

Factory frames the AI coding landscape using the Henry Ford analogy. AI assistants that simply speed up line-by-line coding are merely 'faster horses.' The true paradigm shift—the 'automobile'—is delegating entire tasks to autonomous agents, fundamentally changing the developer workflow.

The ideal AI-powered engineering workflow isn't just one tool, but a fluid cycle. It involves synchronous collaboration with an AI for planning and review, then handing off to an asynchronous agent for implementation and testing, before returning to synchronous mode for the next phase.

Unlike typical AI coding assistants that act as pair programmers, Codex's cloud agents allow a single founder to operate like a CEO. You can delegate concurrent tasks—coding, marketing, product roadmapping—to different AI 'employees', maximizing productivity even while you sleep.