The recent sell-off was triggered by the presentation of a new tool for the legal field by the AI startup Anthropic PBC. KeyBanc analyst Jackson Ader noted that “if this is legal technology today, then tomorrow AI could impact sales, marketing, or finance.”
According to Bloomberg, fears of an impending crash have hit the market for large American and European tech companies. The trillion dollars lost in the stock market reflected negative investor sentiment, and even those companies that were previously considered winners during the AI boom began to show signs of fatigue, lacking new ideas for growth.
"The rout in the stock and credit markets this week is unprecedented in scale," the agency notes in its report.
The decline in the AI market is characterized by its speed and scale. In a short period, hundreds of billions of dollars were wiped off the market capitalization of stocks, bonds, and loans of companies operating in Silicon Valley. Leading software companies found themselves at the center of this crisis, losing nearly a trillion dollars in market capitalization over the past five days.
This situation represents not just panic, as seen in previous crises, but a real threat to the business models of many companies, as was predicted earlier.
The crisis has spread across all continents, including the USA, Europe, India, and China, affecting investors and creditors who previously actively invested in tech companies. Over the past four weeks, loans to American tech companies totaling more than $17.7 billion have fallen to concerning levels.
"The year will be interesting and tense. We are currently witnessing the early stages of rethinking who will be the winner or loser," commented Dek Mallarky, managing director of SLC Management.
There are now concerns that the reckoning for debts and obligations may come sooner than expected, leading to a sharp market collapse.
To understand why artificial intelligence could become a catalyst for the next market crash, it is worth looking at historical examples.
In the late 1990s, the world was swept by euphoria surrounding the internet, with new companies emerging before our eyes. Investors were convinced that a new economic era had arrived, where the old rules no longer applied.
However, in 2000, reality caught up with dreams, and the Nasdaq crashed by nearly 80%. Most dot-coms disappeared, but the internet continued to evolve, changing the world. Investors who poured money into the hype lost their funds. Today, a similar situation is observed, only under the banner of artificial intelligence.
AI is a reality; it has the potential to change industries, but investor expectations are once again outpacing actual profits.
The circle of companies dominating AI holds a significant share of the American market. Giants like Apple, Microsoft, Amazon, Alphabet, Meta, Tesla, and NVIDIA are so large that their performance dictates the direction of the entire stock market.
This level of concentration can be dangerous: small setbacks can trigger serious declines when the market depends on just a few companies.
The risk is further amplified by the scale of investments. Companies working in AI are pouring hundreds of billions of dollars into chips, data centers, and cloud infrastructure, exceeding the GDP of many developed countries.
These expenditures not only shape the future but also support the existing economy. Without these investments, economic growth in the USA would appear significantly weaker.
The economy has become dependent on constant investments in AI to maintain the appearance of strength.
If these expenditures are reduced—whether due to disappointing profits or worsening credit conditions—markets may suddenly realize how fragile the system has become.
A serious problem lies in how money circulates within the AI ecosystem. Many companies finance each other through complex investment ties.
Large firms have invested billions in AI startups, which then spend funds on the cloud services and data centers of those same companies, creating a closed capital cycle.
This can lead to artificially inflated valuations, especially in conditions of low interest rates and high optimism.
When financing conditions become stricter, such structures can collapse quickly.
Currently, valuations in the AI sector assume almost perfect outcomes: rapid technological progress and large profits. Many companies have high multiples despite ongoing losses.
History shows that markets do not forgive when expectations are not met.
Typically, crashes do not begin with stock sell-offs but with credit stress. In the US economy, companies and consumers are heavily indebted, and many need to refinance significant amounts of debt at much higher rates.
This compresses profits and increases the risk of defaults. When weak borrowers face problems, creditors begin to retreat, credit becomes hard to access, investments slow down, layoffs occur, and demand falls. Financial stress begins to transition from the markets to the real economy.
Today, there is a threat that such tightening of credit could occur against a backdrop of inflated AI valuations and rising government debt.
In previous crises, governments could intervene to stabilize situations. Today, however, high levels of debt and rising interest rates limit their options.
AI will not destroy the economy, just as the internet did not destroy the world in 2000. But financial markets have once again outpaced reality.
AI has become a driver of market optimism, justifying extreme valuations and masking deeper economic problems.
When expectations change, AI stocks could plummet sharply, and since these companies are at the center of the market, their decline could drag down the entire system—not as a cause of the crisis but as one of its main triggers.
The reason this potential crash could be more dangerous than the crises of 2000 and 2008 lies in the lack of a clean exit.
Interest rates are already high relative to the level of debt, and central bank balances are already overloaded.
Governments are already facing large deficits, and the usual tools have been used repeatedly.
If confidence collapses now, policymakers may find that their measures create more problems than solutions.
This does not mean that the system will collapse instantly, but it could lead to prolonged instability, sharp market fluctuations, uneven inflation, social unrest, and geopolitical instability.
