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."

In a rapidly changing AI landscape, don't wait to build. Instead, use this litmus test: if a more intelligent future model would make your project better, build it. If a smarter model would render your project obsolete (e.g., a complex rules-based automation), your approach is too fragile and should be rethought.

Overly structured, workflow-based systems that work with today's models will become bottlenecks tomorrow. Engineers must be prepared to shed abstractions and rebuild simpler, more general systems to capture the gains from exponentially improving models.

Instead of chasing the latest hyped AI model, focus on building modular, system-based workflows. This allows you to easily plug in new, better models as they are released, instantly upgrading your capabilities without having to start over.

While building intricate frameworks (scaffolding) to correct model behavior is effective now, it may become obsolete. The speaker suggests it's better to focus on giving models more fundamental capabilities and trust that future, more generalized models will handle tasks without needing such hand-holding.

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 This prevents creating an application that is quickly outdated.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.

The pace of AI model improvement is faster than the ability to ship specific tools. By creating lower-level, generalizable tools, developers build a system that automatically becomes more powerful and adaptable as the underlying AI gets smarter, without requiring re-engineering.

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.