Companies’ AI Claims: A Cautionary Tale of Unfounded Promises

The buzz surrounding artificial intelligence (AI) is akin to the dot-com frenzy of the late ’90s. Companies today tout their AI capabilities as a silver bullet that will make processes better, faster, and cheaper—yet they offer scant evidence to back up these grandiose claims. Just as the excesses of the tech bubble led to a disillusioned reality, we are witnessing a repeat of history with AI, and the implications for investors and the economy are grave.

The Dot-Com Bubble: A Lesson Ignored

Take a step back to March 2000; Gary Smith stood apart from a conference buzzing with optimism about the U.S. stock market. At a time when the DJIA was under 12,000, some experts boldly predicted that it would soar to 36,000. Yet, Smith defied the prevailing cheerleading and indicated that stock prices were inflated. His perspective—that we were in a bubble—went unheeded.

Monday following the conference bore the brunt of Smith’s warning; the Nasdaq Composite dipped, eventually crashing a staggering 75% in the ensuing three years. The takeaway? Investors and analysts alike often ignore significant indicators in favor of enticing narratives, much like the speculation that drove the dot-com boom. Many of those investors placed value on abstract metrics like web traffic and “hits,” neglecting concrete fundamentals such as actual profits.

The Current AI Frenzy

Fast forward to today, where the AI narrative is similarly captivating, promising a revolution as great as the internet boom. The notion that AI can replace human jobs is ingeniously marketed—yet the reality is often less romantic. Academia has fallen into the same trap, stirred by claims of advancing technology; from the sensationalist titles like “Race Against the Machine” to the doomsday scenarios proposed by various scholars, the story has become increasingly disconnected from reality.

In a world where even esteemed professors assert that job categories like surgery and finance are vulnerable to automation, the flaws in their arguments become evident. For instance, the belief that deep learning would outpace radiologists has proven to be misguided; the number of employed radiologists continues to increase. In fact, barring considerable economic downturns, U.S. employment figures have risen consistently, contradicting the extravagant claims of mass job displacement.

A Lack of Profits

Consider the current hype surrounding AI—a rush to label various companies as “AI pioneers” without substantiating their claims with tangible outcomes. In the rush to capitalize on this AI revolution, companies make boastful statements about their efficiencies and the transformation they purportedly bring. Unfortunately, the underlying financials tell a different story. Analysts like Sequoia Capital’s David Cahn and Goldman Sachs’ Jim Covello point out that the revenue and profits associated with AI initiatives are starkly low; significantly less than what dot-com companies commanded back in the early 2000s.

Comparative Revenue Analysis

To put things in perspective, projected revenues derived from AI companies for 2024 hover between $10 billion to $30 billion. In contrast, internet subscriptions in 2000 accumulated around $850 billion, while e-commerce was responsible for approximately $500 billion in revenue—40 to 50 times greater than today’s data regarding AI revenue.

The Illusion of AI Supremacy

The present environment echoes the past: Investors are eager to embrace “story stocks,” where the promise of great efficiency overshadows the meticulous examination of actual profits and stability. This storytelling allure bears remarkable resemblance to the dot-com era—where just adding “dot-com” to a company’s name led to a doubling of stock prices. Today, it seems just associating with AI grants companies the same exponential valuation boosts.

The Verdict: A Coming Bubble?

It is prudent to question whether we are on the brink of an AI bubble—one similar to that of the dot-com craze. The data suggest that the narratives being spun, though compelling, remain largely unsubstantiated. With the tide of financial realities revealing meager profits and revenue across the so-called “AI firms,” investors would do well to scrutinize the facts closely instead of succumbing to the latest technological fairy tale. History has a way of repeating itself, and those who ignore its lessons may find themselves confronting a familiar reckoning.