When building consumer AI applications, founders shouldn't be constrained by today's models. The advice is to anticipate rapid model improvement and design products for capabilities that will exist in the near future, a strategy described as "skating to where the puck is going."

To avoid building products that will quickly become obsolete, Anthropic's PMs design for future model capabilities. By asking, "What will Claude 8 enable for users?", they make architectural and UX decisions today that will be compatible with AI's exponential progress.

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.

To create a breakthrough AI product, design its capabilities around the projected power of models six months out. This means accepting poor initial performance, but ensures you'll be perfectly positioned when more capable models are released.

Building an AI-native product requires betting on the trajectory of model improvement, much like developers once bet on Moore's Law. Instead of designing for today's LLM constraints, assume rapid progress and build for the capabilities that will exist tomorrow. This prevents creating an application that is quickly outdated.

The "bitter lesson" of AI applies to product development: complex scaffolding built around model limitations (like early vector stores or agent frameworks) will inevitably become obsolete as the models themselves get smarter and absorb those functions. Don't over-engineer solutions that a future model will solve natively.

In the fast-paced world of AI, focusing only on the limitations of current models is a failing strategy. GitHub's CPO advises product teams to design for the future capabilities they anticipate. This ensures that when a more powerful model drops, the product experience can be rapidly upgraded to its full potential.

When developing AI-powered tools, don't be constrained by current model limitations. Given the exponential improvement curve, design your product for the capabilities you anticipate models will have in six months. This ensures your product is perfectly timed to shine when the underlying tech catches up.

In the rapidly advancing field of AI, building products around current model limitations is a losing strategy. The most successful AI startups anticipate the trajectory of model improvements, creating experiences that seem 80% complete today but become magical once future models unlock their full potential.