The rapid rise of artificial intelligence (AI) echoes the electrification boom of the 1920s, a period that ultimately led to economic upheaval. As AI technology gains traction, it brings with it the potential for transformative change across various sectors. However, the historical parallels to the devastating market crash of 1929 raise significant concerns about the sustainability of this technological momentum.
The Electrification Boom and Its Fallout
In the early 20th century, the electrification boom marked a pivotal shift in the United States, laying the groundwork for a century of industrial dominance. Investors flocked to companies like General Electric and AT&T, which harnessed electricity to innovate and expand. This surge in investment was fueled by the promise of automation and increased productivity, with even Soviet revolutionary leader Vladimir Lenin emphasizing the importance of electrification as a cornerstone of progress.
As excitement surrounding electricity grew, the concentration of market power became evident. By September 1929, utilities accounted for a staggering 18% of the New York Stock Exchange, with a mere handful of holding firms controlling the majority of the market. Much like the current landscape of AI, the electrification sector existed within a broader ecosystem, with industries across the board relying on electricity for advancements. The Dow Jones launched the Dow Jones Utilities Average to reflect this new sector’s significance, further solidifying the market’s focus on electrification.
Lessons of the Past Resonate Today
The exuberance surrounding electricity stocks mirrored the current enthusiasm for AI investments, where many companies are attempting to become “AI-enabled.” Just as the market was poised for a crash in the late 1920s, the AI sector is beginning to show signs of overvaluation and speculative behavior. The Dow Jones Utilities Average peaked at 144 in 1929, only to plummet to 17 by 1934, leading to widespread economic devastation.
The Great Crash initiated a banking crisis, triggering a cascade of business failures and an alarming rise in unemployment, which soared from 3% to 25% by 1933. The repercussions were felt globally, with many countries, particularly those dependent on international trade, experiencing increased job losses. The once-promised age of leisure and progress devolved into hardship, with soup kitchens and bread lines becoming symbols of the era.
As the failures of the 1920s revealed systemic flaws, significant reforms were introduced. The Public Utility Holding Company Act of 1935 dismantled large holding structures and imposed regional separations, transforming once-exciting electricity companies into regulated entities.
Today, AI is advancing more rapidly than many stakeholders can manage. A few interconnected firms are central to building the current AI infrastructure, raising concerns about the concentration of power and the potential for another economic bust. Regulatory frameworks remain inadequate, with the European Union taking the lead in establishing stricter guidelines, while other regions lag behind.
As society stands on the brink of an AI revolution, the question remains: can we transition to a future where AI operates seamlessly as infrastructure, similar to electricity, without facing another catastrophic downturn? If the lessons from the electrification boom remain unheeded, the risks of repeating history are considerable.
Cameron Shackell, a Sessional Academic at the QUT School of Information Systems, also serves as CEO of Equate IT Consulting, a firm leveraging AI to analyze brands and trademarks. His insights into the evolving landscape of technology and its implications for the economy underscore the importance of understanding historical trends as we navigate the complexities of AI today.
