The AI trade is beginning to look considerably less invincible. More than $1 trillion in market capitalisation was wiped from 20 of the world’s most valuable AI and semiconductor stocks during the week ending October 15, 2026, as investors reassessed the cost of financing the technology boom.

Nvidia, AMD, Micron, SK Hynix, Samsung Electronics, SoftBank and several European chipmakers came under pressure. The sell-off followed hotter-than-expected US inflation data, which fuelled fears that the Federal Reserve could raise interest rates further.

The immediate concern is that building the infrastructure required to power artificial intelligence is becoming extraordinarily expensive. Data centres, advanced chips, memory, electricity and computing capacity require enormous upfront investments. When government bond yields climb above 5%, borrowing becomes more expensive, putting pressure on companies that need years of growth to justify their spending today.

But there is another concern. Manufacturers are expanding production to meet AI demand, while cheaper Chinese memory chips and increasingly competitive AI models threaten to change the economics of the industry. If supply grows faster than demand, or customers become unwilling to pay premium prices, today’s investment boom could produce disappointing returns.

None of this proves that the AI bubble has burst. The sell-off could reflect rising financing costs rather than weakening demand. Yet the scale of the market reaction raises an uncomfortable question: what happens if investors eventually decide that the profits promised by the AI revolution cannot justify the money being poured into it?

The IMF Is Warning About More Than Overvalued Stocks

The International Monetary Fund’s warning takes the debate beyond whether technology shares have become too expensive. Its concern is that a sharp reversal in AI investment could expose weaknesses across financial markets and potentially transmit losses into the wider global economy. The problem is not simply that investors might lose money on a few expensive stocks. It is that the AI boom has created financial connections that could spread the damage.

One concern is the growing concentration of market gains in a relatively small group of technology companies. When a handful of businesses account for a substantial share of stock-market performance, a sharp decline in their valuations can drag down major indices, affect investment portfolios and weaken confidence. The impact could extend to pension funds, institutional investors and households whose savings are exposed to equity markets.

The IMF has also spotlighted the risks created by interconnected financing arrangements. AI companies may invest in one another, purchase each other’s services, commit to future spending and depend on the same investors or lenders. These relationships can support rapid expansion during a boom. But if one company cuts spending or struggles to raise money, the consequences can travel through its suppliers, customers and financing partners. A slowdown at one point in the chain can become a problem for several businesses.

Debt adds another layer of vulnerability. Governments are already dealing with substantial borrowing requirements, while rising bond yields can increase financing costs across the economy. Companies building expensive AI infrastructure face a similar challenge: projects that looked attractive when money was cheap may become harder to justify when interest rates rise and investors demand stronger returns.

The IMF has also raised concerns about cybersecurity. Financial institutions increasingly depend on shared cloud infrastructure and common technology providers. A serious cyberattack on a critical supplier could disrupt multiple institutions at once, potentially affecting payments, trading and access to financial services.

Importantly, the IMF is warning about vulnerabilities, not declaring that a global financial crisis is inevitable. Banks may be able to withstand a severe downturn, but that does not eliminate the possibility of losses spreading through markets and businesses.

The central question, therefore, is whether the global financial system has become too dependent on AI investment continuing to grow at its current pace.

If spending slows sharply, the consequences could extend well beyond Silicon Valley –  and economies such as India, which depend on foreign capital and global technology demand, would have reasons to pay attention.

Ray Dalio, Michael Burry and Wall Street Are Sounding Different Alarms

The warnings are coming from investors who have spent decades studying financial markets, but they are not all predicting the same thing.

Ray Dalio sees the familiar ingredients of a classic bubble. Michael Burry believes investors may be underestimating the danger. And Wall Street strategist Joachim Klement has outlined a scenario in which the S&P 500 suffers a severe correction.

Their arguments differ, but they converge on one concern: the money flowing into AI may be getting ahead of the returns it can realistically deliver.

Dalio’s warning centres on the combination of elevated asset prices, debt and rising interest rates. In his view, a boom becomes vulnerable when investors keep buying because prices are rising, while financing becomes increasingly dependent on borrowed money. Eventually, investors may need cash, lenders may become less accommodating and asset sales can push prices lower. Dalio has also pointed to the possibility that investors will sell assets to meet financial obligations, including taxes on wealth, potentially adding pressure to markets.

Burry, known for anticipating the US housing-market collapse before the 2008 financial crisis, has warned that markets could remain in denial about the AI bubble for months before the risks become harder to ignore. But his reputation from the housing crisis does not guarantee that his current assessment will prove correct. Predicting that a market is overvalued is not the same as predicting when it will fall.

Klement, head of market strategy at Panmure Liberum, has offered a more specific downside scenario. He has forecast that the S&P 500 could fall to 5,000 points by the end of 2027, implying roughly 36% downside from the level cited in his report. His concern includes the pressure that enormous infrastructure investments place on the cash flows of major technology companies as borrowing costs rise. This is his forecast, not a consensus prediction or an IMF projection.

There is also a counterargument. Legendary investor Peter Lynch’s approach emphasised company fundamentals and warned against trying to time market corrections. Investors who sell too early because they expect a crash can miss substantial gains if markets continue rising.

That disagreement matters. The warnings deserve attention, but they do not establish that a crash is imminent. The real question is whether the underlying financial and business conditions are deteriorating enough to justify the pessimism.

The AI Boom Has a Financing Problem

Building the infrastructure needed for artificial intelligence requires extraordinary amounts of capital. Technology companies are committing billions of dollars to data centres, advanced processors, memory chips, electricity supply and computing capacity. These investments must often be made years before the businesses using that infrastructure can demonstrate whether the spending will generate adequate returns.

That creates a vulnerability when interest rates rise. Higher borrowing costs make new projects more expensive and increase the burden on companies that have already taken on debt. Investors also become less willing to pay premium valuations for businesses whose expected profits lie far in the future. A company can therefore face pressure from two directions at once: its financing becomes more expensive while the market value of its future earnings declines.

The proposed financing arrangements involving SpaceX illustrate the scale of the commitments being discussed. BeInCrypto reported that SpaceX was seeking $40 billion in additional debt to purchase Nvidia chips, with a proposed structure involving bank loans and bonds. The financing should be treated as a reported proposal, not as proof that the entire amount has been borrowed. Nevertheless, it highlights the relationship between the companies supplying AI infrastructure and those taking on substantial commitments to acquire it.

This relationship can create a misleading impression of how healthy the wider ecosystem really is. A chipmaker may report strong demand because customers are spending heavily on computing capacity. But that does not automatically mean every customer can generate enough revenue from the infrastructure to repay its borrowing and earn an acceptable return.

The International Monetary Fund’s concerns about interconnected financing add another layer to the problem. When businesses invest in one another, purchase services from one another and rely on the same pools of capital, the system can become vulnerable to a reversal in sentiment. If one company reduces spending, the effect can reach its suppliers and financing partners.

There is also a question of future demand. If companies have built more computing capacity than customers ultimately need, they may struggle to recover their investments. Falling GPU rental prices, cancelled contracts or cuts to planned capital expenditure would be signs that demand is no longer keeping pace with expectations.

The danger is not that borrowing to build AI infrastructure is inherently unsound. It is that debt magnifies the consequences when investments fail to generate the returns on which their financing depends. If that happens across several interconnected companies, a slowdown in AI spending could become a much wider financial problem.

Anthropic, OpenAI and the Question of Who Will Actually Make Money

The biggest question hanging over the AI boom is whether the companies attracting enormous valuations can eventually generate enough profit to justify them. Rapid revenue growth is encouraging, but it does not automatically make a business worth whatever investors are willing to pay for it. The cost of developing models, acquiring computing power and competing for customers can consume substantial amounts of money even as sales increase.

Anthropic has become a focal point in this debate. A report carried by Benzinga suggested that the company could pursue a public listing as early as November 2026, with a potential valuation of $2 trillion and fundraising of up to $100 billion. These were reported possibilities, not a completed listing or a confirmed valuation. Nevertheless, the figures illustrate the extraordinary expectations surrounding leading AI companies.

The debate is not simply about whether Anthropic or OpenAI will succeed. It is about how much investors should pay for that success.

Tusk Ventures founder Bradley Tusk questioned whether Anthropic could justify a valuation equivalent to roughly 27 times revenue, arguing that such a price would require sustained and substantial growth. Gabelli Funds portfolio manager John Belton, by contrast, argued that public financial disclosures could help investors judge whether revenue growth supports these valuations.

There is another challenge: competition. AI companies are spending heavily to build increasingly capable models, but cheaper open-source alternatives could make it harder to charge premium prices. If customers can meet many of their needs with less expensive models, some of the expected revenue may never materialise.
The pressure to grow also raises a difficult question about safety. Tusk warned that public companies facing quarterly earnings expectations might feel pressure to release increasingly capable models to maintain growth, even when doing so raises concerns about safety.

None of this proves that AI companies are overvalued. Their technology could create enormous economic value. But the investment case depends on more than technological progress. It depends on whether companies can turn that progress into durable revenue and profits.

Is the Bubble Already Deflating, or Is the AI Boom Still Intact?

For all the warnings, there is a strong argument against assuming that the AI boom is about to collapse. AI computing demand remains substantial, companies continue investing in infrastructure, and businesses are finding new ways to use the technology. A fall in share prices does not necessarily mean customers are abandoning AI or that the underlying technology has lost its value.

Technology analyst Dan Ives has argued that the recent sell-off reflects concerns about financing costs rather than a collapse in demand. Higher Treasury yields make data centres and other capital-intensive projects more expensive to finance, but they do not, by themselves, establish that businesses need less computing power.

Ives has maintained that the AI infrastructure boom remains in its early stages and views the pullback as a potential buying opportunity.

His argument also offers a useful way to test the market’s optimism. He has identified three developments that could change his assessment: hyperscalers cutting their planned capital expenditure, customers cancelling contracts and GPU rental prices falling. These would provide stronger evidence of weakening demand or excess capacity than a stock-market decline alone.

Another possibility is that the bubble, if one exists, is already deflating gradually. Investors could lower the valuations they are willing to pay, companies could moderate spending and weaker businesses could lose funding without the entire technology sector collapsing. Livemint’s Bertie column explores this possibility, challenging the assumption that a bubble must end in a sudden crash.

Retail investors have also continued buying despite the warnings, according to Yahoo Finance’s reporting. That does not prove that the market is fairly valued, but it shows that the bearish view is far from universal.

The distinction is crucial. AI can become a transformative technology even if some companies have attracted excessive valuations. The test is not whether AI has a future, but whether the profits expected from it can justify the investments being made today.

India Is Losing Foreign Money While Building Its Own AI Future

India faces an unusual dilemma in the global AI boom. Foreign investors have been pulling money out of Indian equities while some global capital is finding opportunities in US technology companies and semiconductor businesses across Asia.

At the same time, India is trying to attract billions of dollars into its own AI infrastructure, data centres and semiconductor industry. If the global AI trade turns sour, India could face both risks and opportunities.

Foreign portfolio investors withdrew a reported ₹3.08 lakh crore from Indian equities during the first nine months of 2026, according to figures cited in Business Standard’s reporting. The same report put net selling in the secondary market at ₹3.64 lakh crore through October 12, illustrating how sustained the pressure has been.

These figures use different reporting measures and cut-offs, so they should not be added together. Global investors have been reassessing their allocations as the appeal of AI and semiconductor opportunities abroad competes with India’s investment case.

However, foreign selling does not mean that India’s stock market will necessarily fall whenever American technology shares decline. Indian equities have their own drivers, including corporate earnings, oil prices, interest rates, currency movements and domestic investment. On October 15, for instance, the Nifty IT index gained 2.4% in morning trading, supported by optimism following HCLTech’s results, even as global AI-related stocks came under pressure.

The consequences of a global AI correction would depend on how it unfolds. If investors simply reduce exposure to expensive American technology stocks and look for opportunities elsewhere, India could attract some of that capital. But if the sell-off becomes a broader financial shock, investors could pull money out of emerging markets altogether. A reversal in foreign flows is therefore a possibility, not a guaranteed benefit.

India is also investing in becoming an AI destination in its own right.

The government has approved the ₹10,372 crore IndiaAI Mission, expanded access to shared computing capacity and announced major semiconductor incentives. Its data-centre ambitions are being supported by measures intended to attract global cloud providers, including a tax holiday until 2047 for eligible foreign cloud services using India-based infrastructure for global operations.

These investments could help India capture a larger share of global AI demand. But they also raise questions about the cost of building the infrastructure, the electricity it will consume and the revenue it can generate.

India is therefore not merely exposed to the global AI boom. It is actively investing in its future. The challenge will be to ensure that the money entering the sector produces sustainable economic returns rather than leaving the country vulnerable to the same investment cycle now worrying global markets.

India’s IT Industry Could Be Hit Even If AI Keeps Growing

India’s technology services industry faces a different kind of AI risk from the American companies building chips and data centres. The concern is that AI could allow businesses to complete work faster while reducing the number of billable hours Indian IT companies can charge their clients.

Traditional outsourcing has often depended on teams of developers, engineers and consultants billing for the time spent delivering a project. If AI tools allow a smaller team to complete the same work in less time, clients may expect lower prices. Productivity improves, but the service provider may not capture the full financial benefit.

A report on the September-quarter earnings season cited Kotak Institutional Equities’ estimate that AI adoption could cause gross revenue deflation of around 7% and net deflation of 3.5% in IT services. These are estimates, not realised industry-wide losses. They nevertheless highlight the pressure on a business model built partly around the volume of human work delivered.

The outcome is not necessarily negative for every Indian technology company. AI implementation, model integration, cybersecurity, cloud migration and enterprise consulting could create new sources of revenue. Indian firms that help global clients deploy AI may benefit even as traditional software development becomes less labour-intensive.

Wipro’s latest results offer a useful counterpoint. The company reported a 9.1% rise in revenue for the July–September quarter, to ₹24,753 crore, beating the average analyst estimate cited by Reuters. Stronger European client spending and AI-related opportunities helped support its performance.

The real question for Indian IT is whether new AI-related business can compensate for pricing pressure and lower demand for traditional services. If global companies cut technology budgets during an AI correction, the sector could face an additional challenge. If AI adoption continues and Indian firms capture the implementation work, the transition could instead create new growth opportunities.

India’s exposure is therefore not simply that AI stocks may fall. It is that the technology could change how much clients pay Indian companies for the work they already do – and what they are willing to pay them to do next.

What Would Turn an AI Correction Into a Global Crisis?

A fall in AI stocks would not, by itself, trigger a global financial crisis. The danger would grow if falling valuations coincided with mounting debt, cancelled infrastructure projects, weaker corporate spending and financial institutions becoming less willing to lend. The IMF’s concern is that interconnected financing could transmit losses across companies and markets rather than confining them to a handful of technology stocks.

Cybersecurity adds another potential source of disruption. Financial institutions increasingly depend on common cloud providers and technology infrastructure, meaning a serious attack could affect multiple businesses simultaneously.

For India, the consequences would depend on the scale of the shock. A contained correction could create opportunities for investors and redirect some foreign capital towards Indian equities. A broader crisis could instead trigger further FII outflows, weaken the rupee, pressure corporate earnings and reduce global technology spending. India’s own economic fundamentals would determine how well it absorbed the impact.

The Last Bit, The Warning Signs Worth Watching

The next phase of the AI boom will depend on whether spending continues to translate into sustainable returns. Investors should watch for hyperscalers cutting capital expenditure, cancelled contracts, falling GPU rental prices and evidence that AI companies are struggling to generate enough cash to support their investments.

For India, the important indicators include foreign portfolio flows, IT services earnings, the rupee, and whether new data-centre and semiconductor investments generate commercially viable returns.

The warnings from the IMF, Ray Dalio and Wall Street strategists deserve attention, but they do not establish that a crash is inevitable. AI could transform the global economy while leaving some investors nursing substantial losses.

The question is whether the financial bets being made today can survive the moment when investors demand proof that the promised returns are real. For India, the challenge is to capture the benefits of AI without becoming collateral damage if the global investment boom turns into a bust.