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.

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 This prevents creating an application that is quickly outdated.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.

In the current AI landscape, knowledge and assumptions become obsolete within months, not years. This rapid pace of evolution creates significant stress, as investors and founders must constantly re-educate themselves to make informed decisions. Relying on past knowledge is a quick path to failure.

Unlike traditional SaaS, AI applications have a unique vulnerability: a step-function improvement in an underlying model could render an app's entire workflow obsolete. What seems defensible today could become a native model feature tomorrow (the 'Jasper' risk).

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.

The rapid pace of AI innovation means today's cutting-edge research is irrelevant in three months. This creates a core challenge for founders: establishing a stable, long-term company vision when the underlying technology is in constant, rapid flux. The solution is to anchor on the macro trend, not the specific implementation.

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.