The hype around AI often leads to two sides of the debate: some believe it's overhyped while others think it's underhyped.

AI is experiencing a resurgence similar to past technological revolutions, and it's important not to dismiss its potential too quickly.

The historical pattern of AI development shows that overhyped moments can lead to periods of stagnation or 'AI winters' when expectations exceed the technology's capabilities.

The AI hype cycle is not about AI having zero value; it's about the reaction to fluctuating prices and the sustainability of current investments in AI.

Despite the advancements, society is cautious about adopting AI technologies, as seen with self-driving cars, which are statistically safer but face public skepticism.

The public's response to my predictions has been divided: some dismiss them as crazy, others acknowledge the historical analysis but remain pessimistic, while a third group seeks solutions to the challenges posed by AI.

The early days of AI development were marked by skepticism about the viability of language models, but persistence in technology has proven their value.

The expectation versus reality of AI adoption is often skewed; while expectations are high, the actual capabilities of current models may not meet those expectations, leading to a rapid adjustment in perception as models improve.

There's a huge premium to naivety in AI; approaching industries with a blank slate can lead to accomplishments that others find difficult due to their preconceived notions.