Nasdaq index falls amid concerns over AI spending and data center expansion

Despite the growing availability of artificial intelligence models, turning them into reliable commercial products remains a complex task, especially when enterprises face fragmented data infrastructure.

At the TechSparks 2026 event, Michael Cronin, Vice President of Sales for Asia Pacific, Japan and India at Couchbase, participated in a discussion with Shivani Mutanna, Senior Director of Strategic Content and Video at YourStory. They discussed the transition of AI from the experimentation stage to full production, management challenges, customer experience, and the increasing role of India in AI innovation.

For decades, large corporations have implemented various databases and highly specialized solutions to solve specific problems. While each solved an immediate issue, the result was often a fragmented data layer composed of multiple systems that interact poorly with each other.

Cronin considers this fragmentation a serious obstacle to AI adoption. He cites industry estimates suggesting that 70–80% of AI development projects never reach the production stage, and MIT research showed that 95% of such projects do not become profitable.

Couchbase's operational data platform is designed to address this problem by helping companies consolidate their data infrastructure. Instead of trying to fix everything at once, Cronin advised companies to start small, recommending that founders and enterprises struggling with data layer fragmentation first choose the most critical use cases.

He cited the example of UK retailer Tesco, which has a large Global Capability Center (GCC) in Bengaluru. Many of this retailer's systems negatively impacted the customer experience, especially during peak shopping periods like Black Friday and weekends. Tesco began consolidating its data layer, starting with one instance and one use case before expanding efforts across its entire technology stack. This consolidation allowed the retailer to process millions of transactions per second with less than a millisecond latency. In Cronin's view, this example demonstrates how companies can begin solving the fragmentation problem through one use case before scaling changes across their entire operation.

The pressure associated with developing AI products has prompted many enterprises to prioritize models and applications. However, pilot projects are often conducted on small datasets in controlled environments, which does not prepare organizations for real-world production demands. Cronin pointed to the growing scale of machine traffic. He stated: 'In eighteen months, machine traffic surpassed human traffic on the internet. That is the scale of AI. That is the scale of machine speed at which businesses must operate.'

When AI applications encounter real-world workloads, fragmented data systems can prevent them from functioning reliably at scale. The increasing use of AI agents exacerbates this issue. As agents become more autonomous and take on decision-making functions, enterprises must ensure they can access the necessary data within appropriate governance structures.

Fragmented systems can hinder communication and management, limiting agents' ability to act autonomously. Cronin also urged enterprises to reconsider their approach to allocating AI investment. While models evolve rapidly, corporate data remains a critical asset. He emphasized: 'Models can be rented. The best model today is yesterday's newspaper. The true asset for an enterprise is data.'

As enterprises began to realize that fragmented data was slowing down their AI initiatives, many responded by adding APIs and connectors to link existing systems. However, Cronin warned that this method could create additional complexity instead of solving the core problem. Every extra connection increases latency, risk, and creates another potential point of failure.

Instead, he recommended consolidating the data layer so that AI applications and agents could interact with it more directly, bringing data closer to the customers. According to Cronin, many enterprises, including large GCCs in India, are currently re-architecting to eliminate data fragmentation. Without these changes, further investments in AI development may lead to the same problems.

Data consolidation can also improve customer experience by helping systems handle growing demand, remain available, and respond quickly. As customers expect more from AI-based services, having data at the edge is becoming increasingly important.

Cronin noted that business cultures vary across the Asia Pacific region, but fragmented data infrastructure remains a common challenge for AI adoption. At the same time, India is emerging as a vital hub for AI innovation and decision-making. He noted that 80% of Couchbase's top 50 global customers have a significant presence in India, with much of their AI innovation and leadership based in the country. Couchbase is also expanding its presence in India, moving its Asia Pacific headquarters to Bengaluru. Among Couchbase's corporate clients in India are Air India, Airtel, BigBasket, and Mahindra Finance. In Cronin's opinion, India's role in global business extends beyond cost savings; innovation, leadership, and important business decisions are increasingly happening there. He mentioned Air India's next-generation entertainment platform, which is built on Couchbase. This platform aims to provide passengers with a personalized shopping and entertainment experience even without network connectivity on board the aircraft. For Cronin, this project illustrates how Indian enterprises are using technology to enhance customer experience in real-world operations.

The stock market is showing detachment from large technology companies. For instance, Google experienced its worst day in history, and Intel's shares fell more sharply than during the Nixon administration. A noticeable decline was also recorded by Amazon and Meta this week, leading to a drop in the Nasdaq index, which is only one percentage point away from a correction (a 10% drop from its historical high).

The general reason for this downturn is that creating infrastructure to support artificial intelligence requires extremely high costs, and the current profits of Big Tech giants do not yet justify such colossal expenses. AI companies are increasingly directing more funds to data centers than their own business operations can generate.

However, if the stock market is trying to send a message, the large technology companies are ignoring it. According to JPMorgan data, AI spending reached $1.6 trillion since 2023 and is projected to be $1 trillion annually over the next decade. No changes in these figures were observed this week.

The reason lies in the fact that the market financing these expenditures—the bond market—remains largely passive, while the stock market experiences moderate panic. Lenders are willing to provide debt to large technology companies to pay for technologies that are transforming the global economy.

Thus, the questions the market seems to be asking about AI—whether Big Tech will continue to increase spending (which will certainly happen) and whether AI will pay off in the near future (it seems not)—are misguided. The real question to ask about AI is: how long can the bond market sustain this continuous surge in spending?

This will ultimately determine whether AI becomes a success or a failure.

The Mechanism of AI Calculations

The principle of AI operation is as follows: significant funds are first invested in the necessary infrastructure (servers, chips, data centers, electricity, workforce, and building materials). Then, after launching the data center, AI companies can monetize these investments—approximately three months or within two years, according to Goldman Sachs.

Since profit realization is delayed for such a long period, hyperscalers (large tech players like Google, Amazon, Microsoft, and Meta) have to raise enormous amounts of money in advance and repay it to investors later. 'Enormous' is a very modest description. These four hyperscalers alone account for over a third of all capital expenditures from the S&P 500 over the last 12 months, notes John Higgins, Chief Economic Advisor at Capital Economics.

Therefore, it is expected that one-third of Big Tech's capital expenditures this year will be financed through debt—for example, around $250 billion in bonds from hyperscalers alone, according to Goldman Sachs estimates. This figure is expected to peak next year, reaching about 35 percent of hyperscalers' spending.

However, bond market investors do not have infinite funds, and there is currently intense competition for lending, especially from US Treasury bonds, as the government simultaneously tries to fund social security, healthcare, and an expensive war.

This raises an obvious question: exactly how much money are investors willing to provide to AI companies? Moreover, what can be assumed?

The hypothetical maximum volume of debt the market can absorb is unknown, but Goldman Sachs has calculated the availability of 510 billion in top-tier investment bonds for the largest hyperscalers. This is more than double what they are expected to borrow by the end of the year.

In other words, the largest AI companies still have a significant capacity to raise borrowed funds. The question is not how much, but how long.

The answer depends on two factors: who pays and who receives.

Amanda Linam, Chief Credit Strategist at Goldman Sachs, stated on a recent company podcast: 'We are not concerned about access to capital. We are simply expecting a more nuanced discussion about where and at what price to issue bonds.'

Linam added that private investment funds, venture capital funds, and private equity are ready to take on the main burden of supporting AI development, possessing trillions of additional 'dry powder' funds for financing.

But hyperscalers are not the only market participants. Other companies operate in this space, including non-cloud services. These are highly leveraged firms that rent out computing power to other businesses, and their credit situation is starting to look questionable. Their low-investment-grade bonds offer high yields, which investors are willing to bet on, accepting greater risk for a potentially huge reward.

CoreWeave operates in this market, whose bond yield rose to 15 percent this month amid accumulating losses. Elon Musk stated that the non-cloud services industry, which includes CoreWeave, must undergo a serious cleansing (he called it a 'guppy level death').

Every new technology has winners and losers, and Musk may be right about non-cloud services. It remains to be seen.

Hyperscalers, conversely, appear to be the winners. Revenue from cloud services (a metric Wall Street uses to measure demand for AI) grew by 50 percent in the second quarter. They possess vast contract portfolios and the ability to absorb higher borrowing costs.

These bottlenecks contribute to maintaining high prices, which helped hyperscalers' revenue grow by 19 percent over the last 12 months, according to Higgins. The emergence of agentic AI led to an explosive growth in demand: agent requests consume 15 times more tokens than human-initiated requests, according to OpenRouter.

Higgins noted: 'The prospects for these things do not look overtly bleak, given that AI is gradually being used more widely in the economy.'

The concern is that the cost of borrowing could become insurmountable. And this could change the economy that is currently supporting AI.

Negative sentiment prevalent in the stock market could begin to seep into the bond market, causing bond yields to rise and increasing borrowing costs for AI companies.

Lyman noted that the bond market functions best when the end of the road is visible. Every AI investor believes there is light at the end of the tunnel. We just don't know how long that tunnel is.

Uncertainty about when companies will start generating profits and earning from their massive AI investments could cause the bond market to slow down, she said. Meanwhile, the outlook for the technology itself and the financial health of large companies looks quite strong, noted Stephanie Ariaga, Global Market Strategist at JPMorgan, in a recent investor note.

Warning signs could include a decrease in AI usage by businesses, a reduction in pricing, or a decrease in customer commitments. Such signs have not yet been observed.

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.'