Nvidia's AI Investment Strategy: Prudent Approach or Bubble Risk?

The core business segments of American tech giants—advertising, consumer, and retail services, which support total AI spending of $750 billion this year—are beginning to show signs of deceleration. In some cases, the technology itself, on which companies are placing large bets, is starting to displace their traditional business models.

Earlier this week, Alphabet Inc., the owner of Google, reported that advertising sales through search and other channels—a segment accounting for about half of total revenue—grew the slowest in the last five quarters. Nevertheless, the company maintained its capital expenditure forecast at up to $205 billion for the current year.

Amazon.com Inc. stated on Thursday that growth in both its main online store and advertising business slowed in the last quarter, but the company confirmed its plan for capital expenditures of approximately $220 billion for this year. Microsoft Corp. and Meta Platforms Inc. also maintained their spending forecasts, presenting results that ultimately failed to meet investor expectations.

Earnings reports and subsequent stock sell-offs reveal growing tension surrounding the transition of major tech companies to new artificial intelligence-based products and associated costs. Companies are taking on unprecedented levels of debt, selling equity, entering into multi-billion dollar data center deals, and assuming trillions in future obligations to build global AI infrastructure. Meanwhile, new products are reducing profitability and diverting attention from some legacy offerings.

Investor patience is wearing thin. Alphabet shares plummeted by a record amount on Thursday, losing nearly $500 billion in market value. Amazon shares fell by 7% during trading, and Meta dropped 10% after publishing last week's earnings, when the company warned investors that its new AI agent application, Muse, requires enormous computational power despite plans to invest more aggressively in AI.

Dan Morgan, Senior Portfolio Manager at Synovus Trust, commented on the situation following Alphabet's report: 'The market is perplexed: how will all this pay off?'

Even Microsoft, which demonstrated strong growth in its cloud business and controlled costs, did not escape negative consequences. Initially, the company's stock rose after news that sales in Azure, its closely watched cloud computing division, increased by 48%, reaching $33.2 billion in the quarter ending in September. However, by the close of trading on Thursday, the stock erased all gains and fell by 0.2%.

Amazon's results were more mixed. The company postponed its annual Prime Day from the third quarter of last year to the second quarter of this year, which accounted for some of the slowdown. But analysts had already adjusted their estimates to account for this, and the results were still below expectations. The e-commerce giant's advertising services division showed an 11% growth, the slowest pace since Amazon first separated this category in 2022. Online store business grew by only 2%, a level not seen in over three years as people resumed pre-pandemic consumption habits and online commerce slowed down.

These results overshadowed the growth of Amazon's cloud computing segment and intensified investor doubts regarding its aggressive AI investments. Cloud computing was also a bright spot for Google: sales in this division more than doubled compared to the previous year, and operating income tripled. The division's CEO, Thomas Kurian, joined the earnings call for the first time, emphasizing the importance of this business for Google's future.

However, investors focused more on the problems arising in Google Search, which remains the company's lifeblood. To stay relevant amid the popularity of chatbots like ChatGPT, Google has integrated more AI into its search engine, including a dedicated tab called AI Mode. But the growing popularity of this feature has negatively impacted the profitability of Google Search. Philip Schindler, Chief Business Development Officer, admitted on Wednesday that the firm is seeing a 'decrease in monetization' of queries using AI Mode, leading to further stock decline. Schindler stated that the company is working to attract more advertising in AI Mode and views the product's trajectory optimistically.

This year, Google is struggling in the AI race against startups Anthropic and OpenAI. Bloomberg Intelligence analyst Mandip Singh believes that if the company can regain its footing in AI model development, it will likely resume delivering the expected investor revenues. Singh noted: 'With all these transitions, sometimes we focus too much on the donor business. Given the margin expansion in the cloud business, I believe the combination of cloud and search, once they reach parity, will return their profitability to previous levels.'

Jean Munster, Managing Partner at Deepwater Asset Management, commented on the decrease in AI Mode monetization at Google, noting that launching new ad formats often occurs with low margins that gradually increase over time. A more complex challenge for the firm is creating agents capable of making purchases on behalf of users and earning a commission from these transactions—a business model many in Silicon Valley consider achievable following the success of Meta's personal assistant Muse. Munster concluded: 'The old way of doing business was using blue links or display ads to influence consumer behavior. The new way is how to influence a bot.'

Artificial intelligence (AI) and its integration into the financial sector became a central theme at the Financial Streets Forum 2026. During an interview at this forum, Zhang Ke, Executive Chairman of the Beijing Institute for Frontier Technology Regulation, shared his views with CGTN on the opportunities, risks, and global governance of AI in the financial sector.

Characterizing AI as the driving force of the fifth industrial revolution, Zhang emphasized that the primary value of AI lies in enhancing efficiency. Nevertheless, he warned that fundamental difficulties persist as long as AI models heavily rely on data dependency and adherence to predefined paths.

Addressing the risks of using AI in large-data financial systems, Zhang highlighted two main issues: data privacy and accountability. While data flow is a necessary condition, questions concerning data security, access levels, and public disclosure require the establishment of strict global regulatory frameworks.

Furthermore, despite AI's capability to significantly optimize decision support and research, Zhang cautioned against fully delegating final trading decisions to algorithms. In the event of financial losses, ultimate responsibility must rest with the human decision-makers, not the machine intelligence.

To mitigate these systemic risks, Zhang called for active international cooperation. He compared the need for global AI governance to major international agreements such as the Paris Agreement on climate change and the Stockholm Convention on Intellectual Property. Instead of excessive regulation, governments should collaborate on creating an international 'AI Accord' to standardize disparate rules.

Looking ahead, Zhang noted that AI provides the greatest benefit when used as a strategic consultant for high-level decision-making and group strategy, rather than replacing routine transactional operations. He concluded that human oversight remains indispensable in every critical financial maneuver.

The International Monetary Fund (IMF), in its latest Global Financial Stability Report, has identified a potential correction in artificial intelligence-related assets as a primary risk to financial markets. The Fund forecasts that a sharp downturn in the AI boom could lead to a 0.4% reduction in global economic output and a 1.3% decrease in global investment by 2028. The IMF specifically links particular vulnerability to developed Asian economies.

This IMF risk is prioritized above rising bond yields and cyberattacks. In an accompanying blog post, IMF economists Atanasios Vamvakidis and Jason Wu warned against complacency.

Current Market Conditions and Risks

Currently, AI supports the global economy and financial markets through investments in data centers, chips, and infrastructure, as well as strong profits from companies driving this boom. Markets have shown resilience due to solid business results related to AI. However, if investments prove weaker or less sustainable than expected, or if profits fail to meet forecasts, AI-related stocks and other assets could lose value. The IMF notes that growing debt burdens and concentration of risks could intensify such a correction.

The report shows that the AI boom is increasingly financed through borrowing. The share of major global cloud providers, known as hyperscalers, in issuing investment-grade bonds has risen from 1% in 2024 to 10%. Simultaneously, their free cash flow is shrinking amid increased AI-related expenditures.

Investors are also actively using leverage. The proportion of individual stocks in leveraged Exchange Traded Funds (ETFs) has increased from 16% to 28%. The share of hedge fund equity, combined with borrowed funds, has grown from approximately 10% to 15% of GDP in developed countries.

Circular Financing and Dependency

The IMF is particularly concerned about so-called circular financing within the AI industry. The Fund examined 14 publicly disclosed deals since 2023. Under these agreements, hyperscalers invested about $300 billion in AI developer equity and provided approximately $650 billion in debt financing. In return, recipients committed to spending $1.4 trillion together with their investors in the future, including funds for data centers, computing power, and cloud services.

According to the report, the financed companies have about $320 billion in debt on their balance sheets, part of which is partially guaranteed by investors or off-balance sheet. The IMF explains the problem of mutual dependence created by these agreements. Companies receiving funding are typically in a significantly weaker financial position than the hyperscalers. If one party encounters difficulties, expected investor returns will decline, reducing the value of their stakes and weakening the reliability of loans associated with these deals.

The Fund characterizes this as 'wrong-way risk': investments lose value precisely when the associated income ceases.

Recommendations and Forecasts

To mitigate these risks, the IMF recommends that regulators strengthen oversight of borrowing used to purchase stocks, as well as leveraged ETFs, as they can amplify both market rallies and declines. Where risks are significant, the Fund suggests introducing industry-specific capital requirements and limiting banks' exposure to the AI industry. According to IMF stress tests, global banks will maintain sufficient capital even in a scenario combining an AI market correction with a sharp rise in bond yields. Nevertheless, financial stability risks remain high.

The Fund also warned that governments and central banks have fewer options to respond to a major shock than in most periods over the last two decades, as the space for fiscal and monetary policy has narrowed. IMF Director Kristalina Georgieva also noted in a blog that corporate market valuations are outpacing actual productivity growth.

In addition to market risks, the Fund pointed to the threat of cyberattacks on critical infrastructure and the potential impact of AI on employment. According to the IMF, 40% of jobs worldwide and 60% of jobs in developed countries are susceptible to AI influence. Georgieva called on regulators to enhance supervision and cybersecurity while reforming tax policy to finance worker retraining without restricting innovation.

The report emphasizes, however, that hyperscalers remain highly profitable in their core operations, generate stable cash flow, and possess high credit ratings, while their investors are generally financially sound. Since AI-related profits have repeatedly exceeded forecasts, the price-to-earnings ratio has even decreased.

According to Tech Times, analysts at Goldman Sachs and Morgan Stanley consider concerns about an AI bubble premature, and the JPMorgan indicator system showed that the current AI boom does not yet meet bubble criteria.