Recently, the startup Anthropic PBC, which develops AI solutions, released a new tool for legal activities. This triggered sell-offs, notes KeyBanc analyst Jackson Ader. "Today it's legal tech, and tomorrow it will be sales, marketing, or finance," he added.
Bloomberg also reports that fears of an impending collapse have hit the market for major American and European tech companies. As a result, stocks have lost a trillion dollars in value. Even companies that were long considered to benefit from the AI boom are now showing signs of fatigue. They no longer have impressive innovations, and the time to pay off debts has come.
The rout that has swept through the equity and credit markets this week is unprecedented, the publication states.
The collapse in the AI market has been characterized by its speed and scale. In a short time, hundreds of billions of dollars disappeared from the stock and bond values of companies across Silicon Valley. Software development firms were particularly hard hit, losing nearly a trillion dollars in market capitalization in just five days.
This decline has become not just a consequence of bubble fears, but a real threat that AI could disrupt the business models of numerous companies, a scenario long predicted by experts.
The crisis has affected the entire world—from the USA to Europe, India, and China—and has even touched the sponsors of major tech companies on Wall Street, increasing anxiety among creditors and private portfolio owners. Over the past four weeks, American tech loans in the Bloomberg index have fallen to critical levels, totaling more than $17.7 billion.
We are on the brink of an interesting and tense year, noted Dec Mallarky, managing director of SLC Management. What we are witnessing now is just the beginning of a reorientation towards those who will emerge as winners or losers in this situation.
There are now concerns that the reckoning on debts may come sooner than expected, and all the inflated optimism could end in a sharp market crash.
To understand why AI could become the catalyst for the next market crash, it’s worth looking at history. In the late 1990s, the world was swept up in euphoria over the internet—new companies were emerging literally overnight, and investors believed a new economic era had arrived where old rules no longer applied.
Stocks soared, ignoring real profits, and by 2000 reality caught up with dreams: the Nasdaq index plummeted nearly 80%, and most dot-coms disappeared. Despite this, the internet survived and continued to change the world, but those who invested based on hype lost everything. A similar situation is now repeating itself, but under the banner of AI.
Artificial intelligence is real and powerful; it will indeed change industries, but expectations have once again outpaced actual profits.
A small group of companies, such as Apple, Microsoft, Amazon, Alphabet, Meta, Tesla, and NVIDIA, dominate the AI discussion, and their stocks make up a significant portion of the entire American market. Such a level of concentration is historically dangerous: when the market relies on a few companies, even minor disappointments can lead to major declines.
The threat is exacerbated by the scale of investments: AI companies are pouring hundreds of billions of dollars into chips, data centers, and cloud infrastructure. Over the past year, spending on AI by major tech companies has surpassed the GDP of many developed countries. These expenditures not only shape the future but also support the current economy. Without AI investments, U.S. economic growth would appear significantly weaker.
The economy has become dependent on constant AI investments to maintain the appearance of strength. If these investments slow down due to disappointing profits or a loss of investor confidence, markets may suddenly realize the fragility of the entire system.
At a deeper level lies the issue of money circulation within the AI ecosystem, where many companies finance each other through complex investment ties. Large firms invest billions in startups, which then spend money on the cloud services of the same companies, creating a closed loop of capital where much of the demand is generated internally rather than from end users.
This does not imply fraud, but it can lead to artificially inflated valuations, especially in an environment of cheap money and high optimism. When funding tightens, such structures usually collapse quickly.
Valuations in the AI sector assume nearly perfect scenarios, including rapid progress and enormous profits, even though many companies continue to report losses every quarter. History shows that markets do not forgive when ideal results are not realized.
Crashes typically do not begin with falling stocks but with credit stress. In the U.S., companies and consumers are heavily indebted, and many need to refinance significant amounts of debt at higher interest rates than before. This squeezes profits and increases the risk of defaults, leading to reduced lending, slowed investments, and rising unemployment.
The current risk is that tightening credit could occur against a backdrop of inflated AI valuations, while governments are also deeply in debt. In previous crises, governments could actively intervene to stabilize markets, but now high public debt and rising interest rates limit such actions.
Artificial intelligence will not destroy the economy, just as the internet did not destroy the world in 2000. But the financial sector has once again outpaced reality.
AI has become a symbol of market optimism and a justification for high valuations that mask deeper economic problems. When expectations are revised, AI stocks could plummet sharply, and since they occupy a central place in the market, their decline could trigger a collapse of the entire system, acting not as the cause of the crisis but as one of its main triggers.
The reason this potential crash could be more dangerous than in 2000 or 2008 is that there is no clear path to retreat. Interest rates are already high relative to the level of debt, central bank balances are stretched, and governments are already facing large deficits. If confidence collapses now, policymakers may find that their measures lead to new problems instead of solutions.
This does not mean an immediate collapse of the system, but it heralds prolonged instability, sharp market fluctuations, uneven inflation, social tension, and geopolitical turbulence.
