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.

When working at the frontier of AI, designers must resist the urge to polish every detail. Since underlying models and product shapes change rapidly, time is better spent on future-looking conceptual problems that AI cannot yet solve, rather than on features with a short lifespan.

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 This prevents creating an application that is quickly outdated.

Simply building what users ask for can trap a product in old paradigms, like reinventing Photoshop's lasso tool for an AI context. A successful strategy involves staying slightly ahead of user adoption, introducing new capabilities that fundamentally change their workflow, and guiding them toward a more efficient future.

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.

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.