The shortest boom in history: why the “AI Era” will end sooner than you think. We assume the AI revolution wil

• Thesis: the public “AI Era” will likely be short not because AI fails, but because it saturates everyday life and stops registering as a distinct boom — it becomes infrastructure or “wallpaper” like the internet did [medium].
• Mechanism: rapid adoption → diminishing visible novelty. Media attention, investment mania, and headline-grabbing product cycles collapse as capabilities diffuse into routine tools; growth shifts from spectacular launches to incremental integration and optimization [medium][wikipedia].
• Consequences: attention and capital rotate away from headline AI products toward operations, regulation, and incumbents; visible macro shocks (hype-driven hiring, venture flows) subside even while underlying productivity gains continue. That creates political, labor, and distributional challenges (winners capture more returns) rather than a tech collapse [medium].
• What comes after: a “saturation” era of normalization — AI as embedded infrastructure, steady productivity improvements, stronger regulation and standards, consolidation among platforms, and episodic innovation rather than a continuous boom [medium][wikipedia].

Follow-up Questions:

1. How will this saturation affect startup funding and unicorn formation?

2. What policies can mitigate the distributional harms as AI becomes infrastructure?

3. Which industries will feel the “end of the boom” earliest?

4. How should companies shift strategy from hype to durable productization?

5. Could a new technical breakthrough restart a fresh AI boom?