Unlike traditional engineering, breakthroughs in foundational AI research often feel binary. A model can be completely broken until a handful of key insights are discovered, at which point it suddenly works. This "all or nothing" dynamic makes it impossible to predict timelines, as you don't know if a solution is a week or two years away.

Google's research head distinguishes between innovation—the continuous, iterative process of improvement applied across product and research—and true breakthroughs. Breakthroughs are fundamental shifts that solve problems not previously solvable in principle, such as the Transformer architecture that underpins modern AI.

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.

Successfully implementing AI isn't an overnight process. SaaStr's Chief AI Officer dedicated three months solely to learning and building agents. This focused effort, which feels like a slowdown, creates a "slingshot effect" where productivity and scale later accelerate dramatically.

In a rapidly evolving field like AI, waiting for mature tools is a mistake. The correct strategy is to invest now, assuming that capabilities that are almost working today will be fully functional tomorrow due to exponential, compounding progress.

While AI progress is marketed in revolutionary "step-changes" (e.g., GPT-3 to GPT-4), the underlying reality is more like compounding interest. A continuous stream of small, incremental improvements are accumulating, and their combined effect is what creates the feeling of an exponential leap in capability over time.

In a world where AI implementation is becoming cheaper, the real competitive advantage isn't speed or features. It's the accumulated knowledge gained through the difficult, iterative process of building and learning. This "pain" of figuring out what truly works for a specific problem becomes a durable moat.

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.

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.