In the fast-evolving AI landscape, building for current capabilities means a product will be obsolete upon launch. Ambience actively predicts AI advancements 18 months out and designs its products for that future state, treating the present as a constantly shifting foundation.

Contrary to the "move fast" mantra, Airtable spent two and a half years developing its product before launching. This premeditated, long-term build, which paralleled Figma's early strategy, allowed for a more robust and feature-rich initial offering.

Wiz's product team, trained at Microsoft, avoids building features that only solve for today's customer but break with tomorrow's enterprise giant. This 'infinite scale' mindset isn't about slowing down; it's about making conscious architectural choices that prevent time-consuming and costly refactoring later on.

Runway's founder justified a multi-year, pre-launch build by studying companies like Figma, which took six years to reach $1M ARR. This reframes building deep, foundational products as a test of stamina and team perseverance, not just a sprint based on raw intelligence or speed.

Traditional SaaS development starts with a user problem. AI development inverts this by starting with what the technology makes possible. Teams must prototype to test reliability first, because execution is uncertain. The UI and user problem validation come later in the process.

The traditional SaaS method of asking customers what they want doesn't work for AI because customers can't imagine what's possible with the technology's "jagged" capabilities. Instead, teams must start with a deep, technology-first understanding of the models and then map that back to customer problems.

To keep pace with rapid AI advancements, the company intentionally operates on a two-year horizon for its technology stack. This forces them to be dynamic and adapt to new research, rather than getting locked into outdated architectures, having completed four such evolutions so far.

Most product orgs focus on the 6-12 month medium term, which is the hardest to predict and control. Shopify's design teams are pushed to ignore this messy middle and focus only on the very long-term North Star and the very short-term actions they can take today, creating a more effective planning process.

When generative AI emerged, the team feared their existing product would become obsolete. Instead of retrofitting AI features, they made the strategic decision to rebuild the entire platform from the ground up with AI at its core. This allowed them to realize their long-term product vision.