Economy
Unless you’ve been living under a rock for the past few years, you’ve probably heard about artificial intelligence (AI). In fact, you’re probably sick of hearing about it, as AI has become a major buzzword across multiple different industries over the past several years.
The acolytes of AI claim that AI could revolutionize the world, and the workplace, in the same way that the internet did. Numerous companies have already announced massive layoffs, claiming that they will be able to operate leaner due to cost savings from AI.
But beneath all the hype, there’s a growing sense of unease about the reality of AI. For the vast majority of people who have accessed large language model (LLM) AI agents like ChatGPT, Gemini, and Claude, it’s hard to imagine that these agents will actually be able to shake up the world.
Certainly they’re useful, and can be very efficient in summarizing and processing large amounts of information in a very short amount of time. But to anyone who has seen the mistakes that AI makes, or has seen the vast amount of AI slop that is invading content across the internet, from writing to images to videos, it’s hard to imagine that so much money is being thrown at this type of technology.
Yet there is a vast amount of money that has already been spent on AI adoption, and much more that is ready to be spent. AI requires massive amounts of computing power, which means massive numbers of processing units, memory, storage capacity, plus the physical space, electricity, and water required to house, power, and cool all of this.
Data center construction is becoming a flashpoint in many areas of the country, with AI companies rushing to buy up empty space to build out their data centers. Along with that comes concerns about the impact on groundwater, and the rise in electricity costs faced by consumers who are now competing with AI corporations for limited amounts of grid capacity.
The rush to invest in and build out AI capacity is expected to result in $2.5 trillion in spending in 2026 alone. And future forecasts are for another $7 trillion to be spent by 2030.
The massive amounts of money being spent on AI have understandably led to questions about how well spent this money is. Is it really necessary to spend trillions of dollars building data centers so that consumers can create videos of cats singing Taylor Swift songs to post on social media?
The major question facing the AI industry is how can this technology be monetized. Most casual users of AI use free AI engines that are perfectly capable of doing the grunt work that they need to be done.
Corporations that have adopted AI pay for AI tokens, but many have found that the amount of money they spend on AI computing power really hasn’t provided the amount of cost savings they thought it would.
If reading this brings you back to memories of the dotcom bubble, when every company with a web address claimed to be the next company to revolutionize the market, you’re not alone. Many people have questioned whether AI is just one big bubble waiting to burst.
Looking at the valuations of major AI corporations such as OpenAI and Anthropic, it’s certainly hard to argue that they’re not in bubble territory. These companies are still privately held, although planning IPOs, so traditional measures like P/E ratios are hard to come by.
But Anthropic is valued at over $1 trillion, while OpenAI is valued at over $860 billion. Meanwhile, Anthropic’s estimated losses in 2025 were over $5 billion, while OpenAI’s were over $21 billion.
Compare those numbers to a company like General Motors, which builds cars. GM had a net income of $2.7 billion in 2025, and has a current market capitalization of nearly $70 billion.
That means that these AI companies that are losing billions of dollars every year are valued more than 10 times as much as GM. And that is without having actual audited financial statements, which public companies are required to do.
Once Anthropic and OpenAI file their IPOs, they will be required to submit to the same financial reporting standards as existing public companies. And then we’ll find out just how much money they’re actually losing.
At some point the people who have sunk trillions of dollars into AI are going to want to see a return on their money, and hype and promises can only go so far. AI companies are going to have to start making enough money to justify their high expenditures and valuations, otherwise the bubble could end up bursting.
Knowing how pervasive the internet is today, it’s hard to imagine that the dotcom bubble actually occurred, but it did. Even Amazon, one of today’s global internet behemoths, lost over 90% of its share value during the dotcom bubble.
One of the major characteristics of the dotcom bubble era is what former Federal Reserve Chairman Alan Greenspan referred to as “irrational exuberance.” Venture capital was being thrown at any dot-com company, lured by the prospect of investing in the next Apple or Microsoft.
Standard metrics like P/E ratios were ignored, and the promise of future earnings made up for the lack of actual earnings in the present. Many dotcom companies not only didn’t make profits, they didn’t make money or even have an actual finished product.
The stock market mania, with markets hitting record highs, led many people to quit their jobs and trade stocks to make money. To those who saw similarities between the dotcom mania and the stock market mania of the 1920s, the warning signs were glaringly obvious.
Eventually, the vast amounts of money thrown into the dotcom sector dried up. With no more venture capital coming in, and spending not slowing, many companies simply ran out of cash.
The NASDAQ Index, where many of the dotcom companies were listed, fell 78% from its peak, with stock markets overall losing more than $5 trillion of value. Over half of dotcom companies failed, and those who survived, such as Amazon, Microsoft, and others, survived with significantly lower valuations.
It’s no wonder that so many people see what’s happening in the AI sector today and see parallels between the dotcom bubble and the AI bubble.
During the dotcom bubble the P/E ratio of the S&P 500 peaked at over 44. Today it is 36.4, and looking at P/E charts you can clearly see how overvalued it is.
P/E ratios of major AI darlings are similarly high, with Nvidia reaching nearly 60 last year and Palantir peaking at over 600. Until OpenAI and Anthropic actually make money, they won’t have a P/E ratio.
But when they do, how high could their P/E ratios get?
With more than $7 trillion expected to flow to the AI sector by 2030, what kind of return will be seen from that investment? Could the bubble burst sooner than we expect?
Fed Chairman Greenspan began raising interest rates in June 1999, which some people at the time blamed for popping the bubble. Today, markets expect Fed Chairman Warsh to possibly raise interest rates as early as September.
Could potential Fed interest rate hikes be the needle that pops the AI bubble? Or is the bubble already on its way to bursting, with most people remaining blissfully unaware until the signs become obvious?
Just like the dotcom bubble, the easy money may eventually run out for the AI industry. Like the dotcom companies before them, there is a lot of money pouring into these companies and being used up, with not much revenue to show for it.
Many critics of the AI bubble narrative point out that AI is a transformative industry that will revolutionize the internet. To the argument that AI is in a bubble, they point to the criticism surrounding the internet in the late ‘90s and how wrong some of those critics were.
While it may be true that AI will be transformative, it can be both transformative and in a bubble at the same time. Amazon, Microsoft, Qualcomm, Cisco, and similar companies were massively overvalued during the dotcom era, survived the dotcom bubble bursting, and survived to become major players in the technology sector.
The same could be said about major AI players today. If the AI bubble bursts, some companies will go out of business but others will not. Those survivors could form the backbone of the AI industry in the coming decades.
The question many people have today is, which companies will survive? And, if the bubble bursts, how can they help protect themselves against the potential fallout?
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