We’re surrounded by bold claims about artificial intelligence. “AI will change everything,” we’re told—by executives, investors, and media pundits alike. But how much of this is grounded in reality, and how much is simply the latest iteration of technology hype? As with most things, the answer is not as clear-cut as the headlines suggest.
The Anatomy of a Bubble
It’s not difficult to see the parallels between today’s AI boom and past technology bubbles. Just as we once saw internet startups with little more than a domain name attract millions in funding, we now see AI startups touting “machine learning” or “generative models” for every conceivable problem. Many of these companies have little to show beyond a slick demo and a promise to disrupt established industries.
The financial markets have responded accordingly. Valuations for AI startups often defy logic, driven more by speculation than by sustainable business models or proven results. Investors, afraid to miss out on the next big thing, are piling in. But as we’ve seen before, this exuberance is rarely sustainable.
Some assets are easy to value—cash, land, equipment. Others, like goodwill or brand value, are more nebulous but still find their way onto the balance sheet through standardized methods. AI, much like these intangible assets, is difficult to measure. The real value is often obscured by hype and wishful thinking, making it challenging to distinguish genuine innovation from vaporware.
Where the Hype Meets Reality
If we step back, it’s clear that much of the AI sector is built on shaky ground. Startups are applying AI to increasingly niche activities, often with little technical differentiation or clear path to profitability. The result is an environment where the signal is drowned out by noise, and the true potential of AI risks being overshadowed by a flood of overpromising and underdelivering ventures.
But does this mean AI itself is overhyped? Not necessarily. The technology is advancing rapidly, and its impact is already being felt in meaningful ways.
My Own AI Journey: From Experimentation to Everyday Utility
When ChatGPT first appeared, I approached it as a curiosity. Like many, I experimented with prompts, learning how to coax useful responses from the model. It was impressive, but I quickly encountered limitations—outdated information, occasional hallucinations, and answers that were difficult to verify.
That changed when I discovered Perplexity and especially the Pro version. The ability to access current data and real-time searches, combined with robust citation capabilities, made a significant difference. Perplexity’s approach reduced hallucinations and made it far easier to verify sources. As a result, it began to replace Google as my primary tool for web research. Instead of wading through pages of ads and SEO-driven content, I could get direct, sourced answers that I could trust.
This wasn’t limited to simple lookups. I started using AI to design customized workout routines tailored to my goals and available equipment. When I received new lab results, I used AI to analyze the data, spot trends, and generate informed questions for my doctor—always treating these insights as a supplement, not a substitute, for professional advice. I've used AI multiple times to quickly design unique travel itineraries.
Financial planning was another area transformed by AI. By linking AI to tools like Kubera and ProjectionLab, I could explore “what-if” scenarios with ease. Adjusting RMDs, modeling Roth conversions and the impact on IRMAA, or evaluating the impact of a major purchase—all became straightforward, data-driven exercises rather than hours spent wrestling with spreadsheets.
These are not just novelties. They are practical, everyday applications that save time, improve decision-making, and enhance my ability to manage complex information.
The Coming Reckoning
If we accept that much of the current AI landscape is a bubble, what happens when it bursts? The answer is familiar to anyone who has watched previous technology cycles. Overvalued startups will collapse. Funding will dry up for all but the most promising ventures. Many companies will be exposed as little more than marketing exercises.
But, as with the dot-com crash, the underlying technology will not disappear. The internet did not vanish in 2000; instead, the survivors—those with real product-market fit—emerged stronger and went on to reshape the world. The same will happen with AI. The collapse of overhyped ventures will clear the way for sustainable, impactful applications to thrive.
AI’s Lasting Value: Beyond the Bubble
It’s important to recognize that the value of AI, much like the value of people in an organization, is not always captured on the balance sheet. Traditional accounting fails to recognize the knowledge, creativity, and experience that drive real value, just as it struggles to measure the true impact of AI.
When the bubble bursts, it will be a financial reckoning, not a technological one. The survivors will be those who move past the hype and focus on delivering real, measurable value. For individuals and organizations alike, the challenge is to look beyond the noise and invest in what truly works.
Practical Implications and Takeaways
So where does this leave us? The lesson is not to dismiss AI as mere hype, nor to blindly accept every claim of disruption. Instead, we should approach AI with a critical eye—recognizing both its limitations and its transformative potential.
For my part, AI has become an indispensable tool. It has changed how I conduct research, manage my health, and plan my financial future. But I remain cautious. I verify sources, treat AI-generated insights as starting points, and remember that technology is only as valuable as the problems it helps solve.
The AI bubble will burst, just as others have before it. But the technology itself is here to stay. The real opportunity lies in separating substance from speculation, and in focusing on the practical, everyday applications that deliver genuine value.