One of the most important and often overlooked parts of successful investing is rebalancing. It may not be as exciting as finding the next hot stock, but sometimes the most valuable thing an investor can do is simply take a little money off the table when one area of the market has become disproportionately large. Artificial intelligence is a perfect example.
There is no question that AI has been one of the dominant investment themes of the past several years. Companies involved in chips, data centers, cloud computing and artificial intelligence have attracted enormous amounts of investor capital. At the same time, the industry is committing staggering amounts of money to build the infrastructure necessary to support the next generation of AI. Much of that investment is being financed with debt. Recent estimates show hundreds of billions of dollars of debt and other financing commitments tied to AI related infrastructure and technology. Major technology companies have issued more than $460 billion in bonds, while bank commitments to AI-adjacent companies' infrastructure have also reached hundreds of billions of dollars. That creates an interesting investment question:
What happens if the enormous profits expected from AI take longer to arrive - or don't arrive at all? That question became more relevant recently when several prominent AI leaders began calling for a slower, more cautious approach to AI development because of concerns about the potential dangers of increasingly powerful systems. On September 14, 2026, AI - related stocks fell sharply after industry leaders including Sam Altman, Dario Amodei and Elon Musk publicly discussed slowing development and increasing safety measures. This doesn't mean AI is going away. In fact, AI could ultimately prove to be one of the most transformative technologies of our lifetime.
But investors don't get paid simply because a technology is revolutionary. They get paid when the price they pay for an investment is justified by the profits that investment eventually produces.
If companies have borrowed enormous sums to build AI infrastructure, those loans still have to be serviced regardless of whether the expected profits materialize quickly. If AI spending slows, regulations increase, competition reduces pricing power, or businesses discover that monetizing AI is more difficult than expected, companies could find themselves carrying significant expenses and debt while waiting for the promised returns. This is precisely why rebalancing matters.
Rebalancing isn't about predicting that AI will crash. It's about recognizing that when one part of a portfolio has grown dramatically, it can quietly become a much larger risk than the investor originally intended. The goal isn't to abandon successful investments.
The goal is to prevent success from turning into excessive concentration. A well-balanced portfolio accepts that we don't know which investment theme will dominate the next five or ten years. AI may continue to exceed expectations. Or it may experience a period of disappointment similar to other revolutionary technologies throughout history. We don't need to predict which outcome will occur. Instead, we can manage the risk by making sure that no single investment, sector or technology has become large enough to determine the financial outcome of the entire portfolio.
Sometimes rebalancing means selling what has done well - not because you think it will fail, but because you don't want its success to become your biggest risk.