Scott Galloway, Prof Marketing, NYU Stern • Host, CNN+ • Pivot, Prof G Podcasts • Bestselling author, The Four, The Algebra of Happiness, Post Corona, published an insightful look at artificial intelligence last month. Originally appearing in Medium.com Content repurposed with credit to author here. Five years ago, Nvidia was a second-tier semiconductor company, primarily known for enhancing the resolution of Call of Duty. Today, it is the third-most-valuable company globally, commanding an impressive 80% share in AI chips, the processors driving an unprecedented $8 trillion value creation in history. Since the release of ChatGPT by OpenAI in October 2022, Nvidia’s value has surged by $2 trillion, equating to Amazon’s market worth. Last week, Nvidia reported exceptional quarterly earnings, with its core business of selling chips to data centers experiencing a 427% year-over-year increase. Last year, at Cannes, Jensen Huang introduced himself to author, Scott Galloway, mentioning his admiration for Galloway’s videos. Not recognizing Huang, Galloway offered to take a photo, which Huang accepted before Galloway continued on his way. Since then, Nvidia has added $1.3 trillion in value. Galloway, on the other hand, underwent Ketamine therapy, abstained from drinking for 17 days, and installed a router with YouTube’s help. It’s been a significant year for both. There is widespread consensus on the revolutionary potential of the AI market, which explains the soaring AI stock prices. However, this unanimity raises concerns about a potential bubble. According to Scott Galloway, the situation mirrors the 1630s tulip mania, where people bid up tulips not for their beauty or utility but because they believed they could sell them at higher prices later—a phenomenon known as the “greater fool” theory. This logic also applies to meme stocks, which embody the “greatest fool” theory. Galloway advises skepticism toward any movement urging people to “stick it to the man,” as it often leaves them vulnerable. Galloway describes the dynamics of economy-distorting bubbles, where speculative psychology meets genuine economic potential. Such bubbles grow as increasing stock prices validate assumptions, attracting more speculators. Low-interest rates can fuel these bubbles, which typically have an enduring technology at their core. He draws parallels to previous bubbles: the dot-com bubble, the housing market bubble, and the cryptocurrency bubble, noting that AI appears to follow a similar trajectory. The financial media often debates whether AI represents a bubble or a genuine technological breakthrough. Galloway argues that AI’s economic promise is real, making a bubble inevitable. He cites the rapid increase in market value among AI-driven companies like Alphabet, Amazon, and Microsoft as indicative of an overvaluation bubble. Nvidia, the standout in the AI sector, faces the challenge of maintaining its valuation by dominating another market as significant as AI. Galloway highlights that the current narrative around Nvidia resembles that of Cisco during the dot-com bubble. Both companies were seen as essential investments in their respective eras, but Cisco’s stock eventually crashed along with the broader market. Timing a bubble’s burst is notoriously difficult. Galloway recounts how past investors, like John Paulson and Michael Burry, timed their bets on housing correctly, but others, like Julian Robertson and George Soros, faced significant losses by mistiming the dot-com bubble. He emphasizes that most people cannot predict market turns accurately and advises diversification and caution. Galloway speculates on how an AI market downturn might occur. A significant non-tech company scaling back its AI investments could trigger a chain reaction of declining stock prices and speculative sell-offs. This scenario mirrors the dot-com bubble’s collapse in 2000 and the housing bubble’s burst in 2007. He concludes that while the AI bubble feels more akin to the dot-com bubble than the housing crisis, its growing size could have broader economic repercussions. The AI bubble’s eventual deflation might resemble Cisco’s post-dot-com trajectory, where long-term value persists despite short-term losses. Ultimately, Nvidia’s current status as a “safe” investment suggests that it might offer returns aligned with the market, rather than the spectacular gains of past tech giants like Amazon. Scott Galloway encapsulates this analysis with a warning: when a “sure thing” stock becomes frothy, it is no longer a safe bet. Investors should be prepared for both the potential risks and rewards, securing their metaphorical tray tables as they navigate the turbulent AI investment landscape . 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