Major AI research labs are focused on improving raw model capabilities, not building user-friendly systems. This creates a significant opportunity for startups to build products with superior user experiences and interfaces on top of these powerful models.

Dixon's AI company, Hunch (2008), struggled because its neural networks lacked the necessary GPU computing power to perform magically. The market and technology were simply not mature enough, highlighting the critical role of timing in startup success.

Marc Andreessen observes that once a company demonstrates a new AI capability is possible, competitors can catch up rapidly. This suggests that first-mover advantage in AI might be less durable than in previous tech waves, as seen with companies like XAI matching state-of-the-art models in under a year.

AI tools allow for building at machine speed, enabling competitors to copy new features in weeks. However, user feedback and insight generation remain bound by human speed. This dynamic erodes the traditional 'learning ahead' advantage that fast-moving startups have over incumbents, as the iteration cycle is compressed.

In the SaaS era, a 2-year head start created a defensible product moat. In the AI era, new entrants can leverage the latest foundation models to instantly create a product on par with, or better than, an incumbent's, erasing any first-mover advantage.

In a rapidly evolving space like AI, being the first mover can be a disadvantage if you bet on the wrong technical approach (e.g., fine-tuning vs. application logic). Second movers can win by observing the market, identifying the first mover's flawed strategy, and building a superior product on the correct technical foundation.

Applied AI startups must solve immediate customer problems by building proprietary technology, even if they know it will be commoditized by foundation models in a few years. The strategy is to win customers now with superior tech, building a product and market position that will endure after the technology becomes table stakes.

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.

Companies focused on ML before the GenAI boom built robust platforms and workflows around their models. When new, more powerful models emerged, they could integrate them as an upgrade, leveraging their existing battle-tested infrastructure to scale faster than new, AI-native competitors starting from scratch.