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."

When building at the frontier of AI, it's a valid strategy to ship imperfect, "vibe-coded" features. This approach assumes that rapid, near-future model improvements will clean up imperfections, making it better to launch an imperfect product now rather than wait for perfect model performance that is just around the corner.

Unlike traditional software where features are explicitly coded, frontier AI systems are trained on vast datasets, leading to emergent abilities. Their internal mechanisms are not directly designed, which is why developers struggle to reliably instill intended goals and prevent unwanted behaviors.

The most valuable startup ideas often identify latent problems that markets haven't articulated. This contradicts the idea that a generic AI tool can solve everything, as it requires a founder's unique vision to persuade customers that a previously unimagined problem exists and needs a new solution.

Traditional SaaS development starts with a user problem. AI development inverts this by starting with what the technology makes possible. Teams must prototype to test reliability first, because execution is uncertain. The UI and user problem validation come later in the process.

In AI, low prototyping costs and customer uncertainty make the traditional research-first PM model obsolete. The new approach is to build a prototype quickly, show it to customers to discover possibilities, and then iterate based on their reactions, effectively building the solution before the problem is fully defined.

AI models experience sudden, discontinuous jumps in specific capabilities—a "jagged edge." The product role must shift from following a predictable roadmap to actively discovering these new, often unexpected, abilities and rapidly building product experiences around them.

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.

A new product development principle for AI is to observe the model's "latent demand"—what it attempts to do on its own. Instead of just reacting to user hacks, Anthropic builds tools to facilitate the model's innate tendencies, inverting the traditional user-centric approach.