What slowing cloud growth reveals about the AI boom.

The AI boom: Why warnings of a new tech bubble are growing louder

The rise of artificial intelligence has become the defining economic and technological story of the decade. Investors, governments and businesses have placed extraordinary confidence in the power of machine learning tools to reshape every industry. This confidence has poured billions into companies that describe themselves as AI innovators. The trend has created a belief that the future is secure, profitable and inevitable. Yet that sense of inevitability has prompted a strong counter-argument from one of the most recognisable investors of the modern era.

Michael Burry, known for identifying the subprime mortgage crisis long before it struck, has warned that the present enthusiasm for AI resembles the late stages of the dot-com period. His recent trades, reportedly worth more than one billion US dollars in put options against leading AI stocks, have reignited a debate on whether the world is living through the next major market bubble.

This article examines the claims, conditions and consequences behind the fear of an AI bubble. It explains why comparisons to 1999 are returning, how spending patterns mirror the behaviour of the dot-com years, why cloud growth and valuations are creating concern and what the fallout could be if the AI Boom proves unsustainable. It also looks at whether Burry’s view is credible or whether the wider market sees something he does not.

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A new wave of exuberance

The phrase AI Boom captures the mood driving markets and corporate decision-making. Companies of all sizes have shifted their marketing, product development and investor communications toward artificial intelligence. In many cases, the link between AI and their core business remains unclear, yet the desire to be seen as future-ready has encouraged them to adopt the label. This mirrors a pattern last seen in the late 1990s when firms added the word “internet” to their name to attract funding.

Back then, investors believed that demand for online services would rise forever, regardless of whether the businesses offering them had viable models. Today’s AI landscape carries a similar optimism. It is not unusual to see valuations that assume continuous exponential growth, even though the technology remains in its early practical stages.

This period of exuberance has been fed by enormous capital expenditure from the largest technology companies. They are racing to secure chips, data centres and cloud capacity to dominate AI services. The scale of this investment has raised eyebrows among analysts who recall the infrastructure overspend that preceded the dot-com crash.

During the late 1990s, telecom and tech companies built more fibre-optic networks, servers and speculative platforms than the market could absorb. When reality failed to meet expectations, the collapse was severe. Burry argues that today’s level of spending resembles that earlier frenzy, with companies committing future revenue to systems that may not yield immediate returns.

Slowing cloud growth and rising pressures

One of the indicators drawing attention is the slowdown in cloud growth. Cloud revenue has driven technology valuations for more than a decade. It has been seen as a stable and predictable line of business. However, cloud expansion has begun to cool as customers attempt to reduce costs. Organisations are increasingly reviewing their storage and compute usage, trimming unnecessary services and shifting focus to efficiency. While cloud providers remain profitable, the sharp growth that once supported soaring valuations is no longer guaranteed.

This new environment matters because AI services rely heavily on the cloud. Training large models demands significant computational power, and delivering AI tools to customers often depends on cloud-based infrastructure. If cloud growth slows, companies face a double challenge. They must support ever-rising AI expenses while also managing lower-than-expected cloud revenue. This creates pressure on balance sheets and heightens the risk that valuations may be built on assumptions that no longer hold true.

The stock market has rewarded AI-related companies with historic highs, sometimes pricing them far beyond traditional measures of value. It is not unusual to see firms trading at price-to-earnings ratios several times higher than the broader market. Investors appear to be betting on future dominance rather than current performance. Burry’s argument is that these valuations are fragile because they rely on sentiment. If the market begins to question the sustainability of AI spending or the real profitability of AI-driven products, sentiment could shift quickly.

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Echoes of 1999

The reference to 1999 is not an exaggeration in Burry’s view. The late 1990s saw rapid innovation, excitement over new technology and a belief that the internet would reshape every industry. All these predictions turned out to be correct. The internet did change the world. What was incorrect was the assumption that every company with an online presence would become a long-term winner. Investors chased internet stocks at extreme valuations without understanding their underlying business. When earnings failed to materialise, the market corrected violently. Trillions of dollars in paper wealth vanished overnight.

In the present AI Boom, the same pattern can be seen. Most experts agree that AI will transform industries. The question is not whether AI matters but whether the current investment climate assumes too much too soon. The fear is that companies have rushed to present themselves as AI-driven to attract funding even when their AI capabilities are limited. This creates an environment where form outweighs substance. Burry’s warning is that when the market eventually separates genuine AI leaders from companies that adopted the label for marketing, the correction could be steep.

The broader impact of an AI adjustment

A crucial part of the concern lies beyond stock prices. If the AI Boom proves to be a bubble, the consequences will spread far deeper than technology shares. Modern economies rely on digital services, cloud infrastructure and automated systems. Businesses in every sector are adapting their operations to integrate AI tools. Many have invested heavily in software, data systems and training to prepare for an AI future. If valuations collapse, investment could freeze. This would slow innovation and place strain on companies that have taken on debt to fund AI transformation.

Furthermore, jobs within tech-adjacent fields could be affected. An AI correction would likely lead to hiring freezes, project cancellations and cost-cutting. This happened during the dot-com crash when thousands of workers lost their positions, and entire companies disappeared. Today the risk is broader because the tech sector is far more embedded in the global economy. A downturn in AI-related spending would influence manufacturing, cloud service providers, semiconductor firms, digital marketing agencies and even higher education programmes focussed on AI skills.

The digital economy as a whole could feel the impact. Start-ups that rely on venture capital may find it more difficult to raise funds. Innovation could slow as risk appetite declines. Regions that have invested public money into AI clusters may struggle to justify continued support if private investment recedes. These effects would not be confined to one country. The AI Boom is international. A correction in major markets would ripple across Asia, Europe, the Caribbean and Latin America.

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Is Michael Burry right?

The central question is whether Burry’s warning is accurate or whether the market is correctly pricing future potential. His record is strong. He predicted the housing crisis when few others did. Yet markets operate differently today. AI is not a speculative idea in the way that many internet start-ups were. It is already delivering measurable results in healthcare, logistics, finance and creative industries. Tools such as language models, image generators and predictive analytics are used daily by millions.

Supporters of the AI Boom argue that the current valuations reflect the scale of future disruption. They believe that AI will become a core component of nearly every digital service, which could justify the investment. They also point to the continued development of new models and algorithms with performance gains arriving at a faster rate than many expected. From this perspective, Burry is underestimating the strength of long-term demand.

Critics, however, highlight that technical progress does not always match revenue. They note that AI services remain expensive to run. They also raise privacy, regulatory and ethical issues that could slow adoption. Many businesses are still testing AI tools rather than using them at full scale. If those tests show limited financial returns, investment could fall sharply. This forms the basis of Burry’s view: that the market has assumed too much growth without evidence of stable long-term profits.

The path forward

Regardless of whether the AI Boom proves sustainable or collapses, the debate itself is important. It forces companies to evaluate their spending with greater discipline. It encourages investors to ask for transparency in business models. It pushes governments to consider how to support innovation without fuelling destabilising speculation. These are healthy developments for the global economy.

For individuals, the lesson from past bubbles is that technological revolutions are real but financial manias can distort their value. The internet did change the world, but many of the companies that led the initial wave no longer exist. The same may be true for AI. The long-term winners will be firms with strong execution, efficient infrastructure and genuine innovation. The rest may fall away when market conditions shift.

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Conclusion

The AI Boom has created excitement, opportunity and a sense of limitless potential. At the same time, it has raised warnings from experienced investors who recognise patterns from past eras. Michael Burry’s billion-dollar bet against major AI stocks has reignited discussion about whether the current market resembles the final years of the 1990s. With high valuations, slowing cloud growth and companies racing to present themselves as AI-driven, the comparison carries weight.

Whether Burry is right remains to be seen. What is clear is that the outcome will shape jobs, businesses and the wider digital economy. Understanding the risks behind the hype is essential as AI continues to influence global markets. The question facing investors and companies now is whether they are witnessing the start of a lasting technological transformation or participating in a speculation cycle that will eventually need correcting. Only time will tell which side has read the situation correctly.

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