The early 20th century, marked by post-panic optimism and new technologies, saw financial journalist Alexander Dana Noyes describe a pervasive "New Era" belief that old financial rules were obsolete. This irrational exuberance, however, led to the Panic of 1907, illustrating how "new era" thinking, while benefiting innovators, also empowers marginal players and hucksters, amplifying risk and leading to systemic collapse. Similarly, the 1995 Netscape IPO sparked the dot-com boom, fueled by venture capital and the economic concept of increasing returns in "winner-take-all" markets. This led to deregulation, including the repeal of Glass-Steagall, as investors believed the Silicon Valley model could be applied universally, despite its limited applicability. The bubble burst in 2000, with many companies failing and even conventional businesses like Enron embroiled in scandal.Another instance of "new era" thinking surfaced in the 1960s with the mathematical finance revolution, based on Louis Bachelier's work and Eugene Fama's Efficient Market Hypothesis. This led to theories like CAPM and Black-Scholes, creating a financial engineering industry focused on managing risk. Despite warnings from mathematicians like Benoit Mandelbrot about market volatility and events like the LTCM collapse, the belief in engineering risk out of the system persisted. This illusion shattered with the 2008 financial crisis, which nearly collapsed the global economy.Currently, the rise of AI is instigating a new "new era" of optimism, with massive investments pouring into the technology, surpassing previous booms. AI's ability to perform human tasks is astounding, leading some, like Matt Shumer, to believe their technical work is becoming obsolete. However, academic research from MIT and Nobel laureate Daron Acemoglu indicates minimal overall productivity impact from AI investment. The disparity lies in AI's significant productivity boosts in computer-related jobs versus minimal impact in sectors like manufacturing and leisure.This phenomenon, dubbed the "productivity paradox" in the 70s and 80s with computer technology, highlights that while AI transforms the "world of bits," its effect on the "world of atoms" (housing, food, etc.) remains limited. Despite impressive technologies like the mobile web and cloud computing, productivity growth has been largely depressed over two decades. This suggests that, like previous investment booms driven by "new era" thinking, the current AI boom will likely end in a bust. Innovation is a protracted process of discovery, engineering, and transformation, with the latter taking far longer than anticipated.