Executive Summary:
It has all the drama of “Game of Thrones”: The Magnificent-7 kingdoms, each surrounded by moats, rarely had threatened each other’s monopolies in the past. Now, with the advent of AI, they have been encroaching on each other’s previously sacrosanct fiefdoms, forcing one another to spend ever more to remain in the game. Amid the chaotic disruption, investors’ AI euphoria has given way to AI agita as confidence in the Mag-7 ebbs. Are their earnings inflated by accounting? Will returns justify their capital investments? Our take: AI will have a powerful impact on productivity in the economy. The winners may not be among the Mag-7 at all but the S&P 500’s Impressive 493 and the economy at large. … Today, Dr Ed enlists the help of Google’s Gemini AI assistant to extend the “Game of Thrones” metaphor to this disruption as well as the transition in the Fed’s Iron Throne.
Game of Thrones I: AI Battles & the Roaring 2020s
Our “Roaring 2020s” narrative has been based on a technology-led productivity boom. Artificial intelligence (AI) was a minor part of our story back in 2020 when we developed our base case for the decade ahead. Little did we know that OpenAI would introduce ChatGPT in late 2022, thus becoming a major part of our story.
Our initial premise was that as the Baby Boomers retired, the labor force growth rate would slow. That would exacerbate the mismatch between workers’ skills—especially those of new, younger entrants to the labor force—and employers’ requirements. We concluded that employers would have no choice but to use technology to boost the productivity of their employees. AI boosted our story.
The productivity miracle promised by AI certainly helped to drive the stock market to new record highs this year. But in recent weeks, the market has climbed a wall of worry. Investors’ excitement over AI’s capabilities has given way to concerns about accounting issues, earnings and credit quality, mounting competition, returns on invested capital, and the sustainability of the AI trade.
Investors no longer are asking what AI companies can build but whether they are overpaying for it—and whether expected returns will materialize before the hardware becomes obsolete. A case in point: The debate over
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