The most resilient founders are motivated by something beyond wealth, like proving doubters wrong (revenge) or recovering from a past failure (redemption). This drive ensures they persevere through tough times or when facing a massive buyout offer that a purely financially motivated person would accept.
As AI models become commoditized, the ultimate defensibility comes from exclusive access to a unique dataset. A startup with a slightly inferior model but a comprehensive, proprietary dataset (e.g., all legal records) will beat a superior, general-purpose model for specialized tasks, creating a powerful long-term advantage.
Investors see it as a significant positive signal when a founder can demonstrate a comprehensive understanding of their industry's history, including past failures and adjacent companies. This historical context indicates they have a unique angle of attack and are not simply repeating old mistakes, differentiating them from less-prepared entrepreneurs.
AI enables "software does labor" business models in industries previously deemed too small for specialized software, like dental offices or trial law. By replacing or augmenting specific labor tasks, startups can justify high-value contracts in markets that historically wouldn't pay for traditional SaaS tools.
The core conflict is whether a startup can achieve mass distribution before the incumbent can replicate its core innovation. Historically, incumbents have an advantage because they eventually catch up on technology. AI may accelerate this, making a startup's unique and rapid path to acquiring customers more critical than ever.