AI tools accelerate development. Instead of using this new speed to add more features (increasing scope), designers should leverage it to deepen the craft and quality of the core, essential features, creating an experience users have never seen before.

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.

When working at the frontier of AI, designers must resist the urge to polish every detail. Since underlying models and product shapes change rapidly, time is better spent on future-looking conceptual problems that AI cannot yet solve, rather than on features with a short lifespan.

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.

Modern AI can rapidly build complex products ("zero to n"), but it lacks the human intuition to simplify by removing features. This critical skill, honed through real-world usage and experience, is what prevents products from becoming bloated and unfocused.

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.

Adopt an "unshipping" culture. If a feature doesn't meet a predefined usage bar after launch, delete it. While a small subset of users may be upset, removing the feature reduces clutter and confusion for the majority, leading to a better overall user experience.

In the age of AI, perfection is the enemy of progress. Because foundation models improve so rapidly, it is a strategic mistake to spend months optimizing a feature from 80% to 95% effectiveness. The next model release will likely provide a greater leap in performance, making that optimization effort obsolete.

Unlike traditional software, AI prototypes can be built almost instantly. This requires a mindset shift: if a project doesn't demonstrate tangible value on its very first day, it should be abandoned immediately. Sticking with a weak AI concept leads to costly slow failure.