Like I said several times in that piece, the technology is real. But so is the bubble. Both things are true simultaneously.

But that leaves an obvious question: if AI is genuinely transformative yet wildly overvalued, how should you invest?

Let me show you.

The Dot-Com Lesson Few Remember
Most people instinctively try to pick the winners—figure out which AI companies will become the next trillion-dollar giants and get in early. And sure, it's probably a little too late for the "getting in early" part… but it's still tempting. The upside looks massive.

You've probably heard the success stories from the dot-com era: Amazon (AMZN), Apple (AAPL), Microsoft (MSFT), and Cisco (CSCO). The companies that survived the crash and went on to become trillion-dollar giants. If you'd invested $1,000 into Amazon in 1997, it would be worth roughly $2.4 million today. A $1,000 position in Apple from that same year would have grown into about $1.34 million.

Those are life-changing returns.

But here's the problem: picking winners is only easy in retrospect. Doing it in real-time, in the middle of a bubble, is nearly impossible. And history proves it. For every Amazon that survived, there were dozens—maybe hundreds—of companies that went to zero. And at the time, it was nearly impossible to tell which was which.

Take Pets.com, the poster child of dot-com insanity.

In early 2000, Pets.com was everywhere. Its sock puppet mascot starred in a Super Bowl ad watched by 88 million people. The company raised hundreds of millions from respected venture capitalists. It went public on the Nasdaq in February 2000 with the symbol IPET at $11 per share.

The business model was simple: sell pet supplies online and deliver them directly to customers' doors. Convenient. Modern. The future of retail.

There was just one problem: the economics didn't work. Shipping 40-pound bags of dog food to customers' doorsteps cost more than the revenue it brought in. The company was selling products at nearly 30% below cost, following the mystifying dot-com logic of "we lose money on every sale but make it up in volume."