Assuming software inference expenses will decrease does not guarantee expanding margins because declining compute costs benefit all competitors equally. AI startups demonstrating high gross margins early on offer safer evidence of proprietary defensive value, whereas low-margin companies risk remaining trapped in low-multiple structures.
AI applications deploying extensive human forward-deployed engineers face traditional professional service dynamics. While private markets occasionally award these firms high enterprise multiples, they risk severe compression unless the engineered solution creates deep customer lock-in that enables rapid margin expansion later.
Surviving technological shifts requires leadership willing to discard existing corporate assumptions and destroy current revenue lines to adopt AI. Founder-led teams show significantly higher willingness to take such existential, capital-risking pivots compared to professional hired CEOs.
During periods of intense hype, rapid top-line expansion receives capital indiscriminately. Investors frequently fail to separate high-growth businesses sustained by durable unit economics from those relying on creative accounting or negative gross margins, creating inevitable long-term downside for most funded companies.
Massive funds in the multi-billion-dollar range cannot return capital through typical five to ten billion-dollar exits. To move the needle, managers are mathematically forced into outlier potential companies, fueling fear of missing out and competitive herd behavior at steep valuations.
Because raw code has become trivial to generate quickly, traditional technical lock-in no longer functions as a primary moat. Lasting defensive value in AI shifts toward customer trust, regulatory navigation, strict accuracy requirements, distribution partnerships, and proprietary domain data.