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.

While AI has increased the *quantity* of software being shipped, it has not increased the quality. There's a noticeable lack of reliability and "machined unibody aluminum" engineering craft, even from top AI labs. The industry needs to refocus on quality, not just shipping speed.

The rapid pace of development enabled by AI doesn't eliminate technical debt; it accelerates its creation. More code shipped faster means more potential bugs, maintenance overhead, and architectural risk that must be managed proactively, not just reactively.

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.

AI tools accelerate development but don't improve judgment, creating a risk of building solutions for the wrong problems more quickly. Premortems become more critical to combat this 'false confidence of faster output' and force the shift from 'can we build it?' to 'should we build it?'.

The massive cost of AI infrastructure makes the traditional startup ethos of "move fast and break things" reckless. Wastage costs are too high and margins for error too low. The new imperative is to "move fast with responsible infrastructure," valuing common sense and iterative development over rapid, wasteful scaling.

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.

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.

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.