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Traditionally, implementation was expensive, so teams de-risked ideas with docs. With AI, building is cheap, so teams now create numerous prototypes first and then curate them. The process is now "build then decide," not "decide then build," with curation and taste becoming the most expensive part.

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AI tools democratize prototyping, but their true power is in rapidly exploring multiple ideas (divergence) and then testing and refining them (convergence). This dramatically accelerates the creative and validation process before significant engineering resources are committed.

With AI, the initial effort to explore an idea—like writing the first draft of a spec or building a janky prototype—is now effectively free. This drastically lowers the cost of exploration, but the last 10% of refinement and quality assurance remains the hardest and most critical part.

AI drastically lowers the cost of exploration. The best teams leverage this by building many prototypes and exploring multiple directions, knowing most will be discarded. This 'wasted work' is a sign of effective discovery, leading to better final products.

AI tools are dramatically lowering the cost of implementation and "rote building." The value shifts, making the most expensive and critical part of product creation the design phase: deeply understanding the user pain point, exercising good judgment, and having product taste.

Traditional product development (PRD-first) was designed to protect scarce engineering resources. With AI making software creation as easy as writing a document, teams can shift to a prototype-first approach, where ideas are built and tested immediately without agonizing over ROI.

AI tools dramatically speed up code implementation, making engineering velocity less of a constraint. The new challenge becomes the slower, more considered process of deciding *what* to build, placing a premium on strategic design thinking and choosing when to be deliberate.

Anthropic leverages the low cost of execution in the AI era by building multiple potential product versions simultaneously. This "build all candidates" approach replaces lengthy spec-writing and low-bandwidth customer research, allowing them to pick the best functioning prototype directly.

In AI, low prototyping costs and customer uncertainty make the traditional research-first PM model obsolete. The new approach is to build a prototype quickly, show it to customers to discover possibilities, and then iterate based on their reactions, effectively building the solution before the problem is fully defined.

Companies are using AI tools like Perplexity Computer to build functional MVPs almost instantly. This cultural shift allows teams to interact with a working version of an idea to gauge its value before investing significant engineering resources, replacing the traditional text-based planning phase.

The product management workflow is evolving from documentation to creation. With AI tools lowering the barrier to build, PMs can now develop and share functional prototypes to communicate ideas and test assumptions, a much higher-fidelity approach than traditional written documents.