Instead of building AI models, a company can create immense value by being 'AI adjacent'. The strategy is to focus on enabling good AI by solving the foundational 'garbage in, garbage out' problem. Providing high-quality, complete, and well-understood data is a critical and defensible niche in the AI value chain.

AI favors incumbents more than startups. While everyone builds on similar models, true network effects come from proprietary data and consumer distribution, both of which incumbents own. Startups are left with narrow problems, but high-quality incumbents are moving fast enough to capture these opportunities.

The future of AI isn't just in the cloud. Personal devices, like Apple's future Macs, will run sophisticated LLMs locally. This enables hyper-personalized, private AI that can index and interact with your local files, photos, and emails without sending sensitive data to third-party servers, fundamentally changing the user experience.

An emerging AI growth strategy involves using expensive frontier models to acquire users and distribution at an explosive rate, accepting poor initial margins. Once critical mass is reached, the company introduces its own fine-tuned, cheaper model, drastically improving unit economics overnight and capitalizing on the established user base.