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.

The exponential improvement of AI models means product development must target future capabilities. Leaders should anticipate that problems taking months to solve will soon be trivial, and build roadmaps that assume a 6-12 month leap in technology.

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 This prevents creating an application that is quickly outdated.

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.

AI is evolving so rapidly that building for today's limitations is a mistake. Leaders should anticipate the state of the technology six months in the future and design products for that world. This prevents being quickly outdated by the pace of innovation.

Building AI applications is a moving target. Engineering solutions to compensate for current model deficiencies (like limited context windows) is often wasted effort, as future models will likely solve those problems. The key is to anticipate the capabilities of the model you'll have at launch and not bet against its progress.