Two models to address how/why/when AI companies make sense: (1) High integration (read: switching) costs: any deployment of real value is carefully tested and

Two models to address how/why/when AI companies make sense: (1) High integration (read: switching) costs: any deployment of real value is carefully tested and tuned for the use-case (support for product x, etc.). The use cases typically don't evolve that much, so there's little benefit to re-incurring the cost for new models. Hence, customers stay on old technology. This is the rule rather than the exception e.g., in medical software. (2) The Instagram model: it was valuable with a tiny number of people because they built technology to do one thing wanted by a slice of the market that was very interesting to the big players. The potential of the market set the time value of the delay in trying to replicate their technology, at some risk of being a laggard to a new/expanding segment. The technology gave them a momentary head start when it mattered most. Both cases point to good product-market fit based on transaction cost economics, which leads me to the "YC hypothesis": The AI infrastructure company that best identifies and helps the AI integration companies with good product-market fit will be the enduring leader. If an AI company's developer support consist of API credits and online tutorials about REST API's, it's a no-go. Instead, like YC and VC's, it should have a partner model: partners use considerable domain skills to build relationships with companies to help them succeed, and partners are selected and supported in accordance with the results of their portfolio. The partner model is also great for attracting and keeping the best emerging talent. Instead of years of labor per startup or elbowing your way through bureaucracies, who wouldn't prefer to advise a cohort of the best prospects and share their successes? Unlike startup's or FAANG, you're rewarded not for execution or loyalty, but for intelligence in matching market needs. So the question is not whether the economics of broadcast large models work, but who will gain the enduring advantage in supporting AI eating the software that eats the world?