During a major technology shift like AI, the most valuable initial opportunities are often the simplest. Founders should resist solving complex problems immediately and instead focus on the "low-hanging fruit." Defensibility can be built later, after capitalizing on the obvious, easy wins.

Unlike traditional software development, AI-native founders avoid long-term, deterministic roadmaps. They recognize that AI capabilities change so rapidly that the most effective strategy is to maximize what's possible *now* with fast iteration cycles, rather than planning for a speculative future.

Instead of waiting for AI models to be perfect, design your application from the start to allow for human correction. This pragmatic approach acknowledges AI's inherent uncertainty and allows you to deliver value sooner by leveraging human oversight to handle edge cases.

Many voice AI products fail by tackling too broad a problem. April's success came from focusing intensely on a limited set of high-value use cases (email, calendar), which allowed them to build a product that "just works" and feels human-like, driving retention.

Incumbents face the innovator's dilemma; they can't afford to scrap existing infrastructure for AI. Startups can build "AI-native" from a clean sheet, creating a fundamental advantage that legacy players can't replicate by just bolting on features.

The founder of Stormy AI focuses on building a company that benefits from, rather than competes with, improving foundation models. He avoids over-optimizing for current model limitations, ensuring his business becomes stronger, not obsolete, with every new release like GPT-5. This strategy is key to building a durable AI company.

Perplexity's CEO argues that building foundational models is not necessary for success. By focusing on the end-to-end consumer experience and leveraging increasingly commoditized models, startups can build a highly valuable business without needing billions in funding for model training.