Instead of waiting for AI models to be perfect, design your application from the start to allow for human correction. This pragmatic approach acknowledges AI's inherent uncertainty and allows you to deliver value sooner by leveraging human oversight to handle edge cases.

Optimal AI workflow involves humans acting as the "bread" on either side of the AI's work. A human first sets the frame and defines "good," the AI then executes the core task (drafting, coding), and finally, a human judges the output and decides the next steps. This structure ensures quality and strategic direction.

AI loops and tools like `/goal` are effective for quickly building experimental prototypes where fine details are unimportant. For building a polished product where details and unique "sauce" matter, the human-in-the-loop approach remains superior and more cost-effective.

With AI agents capable of generating code and designs at an unprecedented rate, the new chokepoint in workflows is human review. The primary challenge is no longer production but scaling the evaluation process to ensure AI-generated output aligns with quality standards and company values.

Even when AI automates complex workflows, a human is still required to provide the initial prompt and direction. The nature of work shifts from manual execution to high-leverage direction, but the human role remains critical.

AI can rapidly execute the 'build' and 'measure' steps of a feedback loop, but true 'learning' is still done by the human founder. Offloading the entire process to AI without deep personal engagement will slow you down, as the machine cannot replicate the founder's capacity for insight.

AI's true productivity leverage is not just speed but enabling more attempts. A human might get one shot at a complex task, whereas an AI-assisted workflow allows for three or more "turns at the wheel." The critical human skill shifts from initial creation to rapid review and refinement of these iterations.

Instead of perfecting a single prompt, treat AI interaction as a rapid, iterative cycle. View the first output as a draft. Like managing an employee, provide feedback and refine the result over several short cycles to achieve a superior outcome, which is more effective than front-loading all effort.

Contrary to the goal of full automation, the most effective AI workflows intentionally preserve points of friction. These moments—where a human must intervene, check intent, or re-steer the process—are crucial for maintaining control and ensuring the output aligns with strategic goals, preventing the system from running unchecked in the wrong direction.