Applying the historical pattern, the current, all-consuming AI zeitgeist is imprinting on today's 20-year-olds (born in 2005). This theory predicts they will emerge as the dominant cluster of leaders in AI and AI-adjacent fields within the next two decades, around 2045.

After years of inflated promises, the market is moving past the initial AI hype cycle. Leaders realize that simply attaching "AI" to a company name is not a strategy. This shift leads to a more realistic understanding of where AI provides practical value, which will stabilize hiring and investment.

Frame AI as a fundamental productivity shift, like the personal computer, that will achieve total market saturation. It's not a speculative bubble but a new, permanent layer of the economy that will be integrated into every business, even a local taco truck.

New research shows ~30% of American teens use AI chatbots daily, compared to only 10% of working adults. This creates an impending skills gap, with an AI-native generation poised to enter a workforce where the majority of incumbents have dramatically less experience with the technology.

While AI-native, new graduates often lack the business experience and strategic context to effectively manage AI tools. Companies will instead prioritize senior leaders with high AI literacy who can achieve massive productivity gains, creating a challenging job market for recent graduates and a leaner organizational structure.

The hype around an imminent Artificial General Intelligence (AGI) event is fading among top AI practitioners. The consensus is shifting to a "Goldilocks scenario" where AI provides massive productivity gains as a synergistic tool, with true AGI still at least a decade away.

The risk of an AI bubble bursting is a long-term, multi-year concern, not an imminent threat. The current phase is about massive infrastructure buildout by cash-rich giants, similar to the early 1990s fiber optic boom. The “moment of truth” regarding profitability and a potential bust is likely years away.

The true threshold for AI becoming a disruptive, "non-normal" technology is when it can perform the new jobs that emerge from increased productivity. This breaks the historical cycle of human job reallocation, representing a fundamental economic shift distinct from past technological waves.

Brian Chesky applies the classic "overestimate in a year, underestimate in a decade" framework to AI. He argues that despite hype, daily life hasn't changed much yet. The true shift will occur in 3-5 years, once the top 50 consumer apps are rebuilt as AI-native products.