When building at the frontier of AI, it's a valid strategy to ship imperfect, "vibe-coded" features. This approach assumes that rapid, near-future model improvements will clean up imperfections, making it better to launch an imperfect product now rather than wait for perfect model performance that is just around the corner.

For AI products, the quality of the model's response is paramount. Before building a full feature (MVP), first validate that you can achieve a 'Minimum Viable Output' (MVO). If the core AI output isn't reliable and desirable, don't waste time productizing the feature around it.

Anthropic prototypes features like code review even when model accuracy is too low for a public launch. This allows them to identify what's missing and be ready to immediately swap in a new, more capable model to close the gap and launch ahead of competitors.

Unlike traditional software development, where consistency is paramount, AI development requires testing many ideas quickly. Anthropic intentionally launches overlapping features to see which form factor users prefer, accepting the cost of a less consistent UX in exchange for speed and market feedback.

While AI dramatically increases development speed, it's a double-edged sword. Without a solid product foundation, user understanding, and clear principles, teams will simply accelerate the shipment of low-value features. AI amplifies both good and bad practices.

AI code generation tools have made building a Minimum Viable Product (MVP) trivial. The MVP no longer signals a team's engineering prowess to investors. Its primary function is now to rapidly generate infinite variations for customer feedback, not to prove technical competence.

Years of focusing on MVPs has weakened the ability of product teams to imagine magical, delightful features. AI prototyping tools make ambitious ideas easier to build, helping teams reignite their creative muscles and aim for awesome products, not just viable ones.

Fable 5's extreme thoroughness, while powerful, makes it unsuitable for tasks like writing product specs. Its outputs are too dense and detailed, missing the bigger picture in a way that can delay shipping. Sometimes a "dumber," more pragmatic approach is more effective for product development.

Building a product too quickly with AI, without incremental user feedback, is like growing a tree indoors without wind. It appears fully formed but lacks the structural integrity and deep intuition gained from being exposed to real-world forces and user friction at each stage of growth.