The substantial investment in data centers has been a primary driver behind the historic surge in information technology spending. In the current landscape of intensifying competition and increasingly scarce resources, expenditures related to data centers have become almost the sole pillar supporting industry growth.

This perspective is one market interpretation of the events following IBM's profit warning on Tuesday, which triggered a significant stock price drop. If CEO Arvind Krishna's assessment—that the weak performance is attributable to broader market conditions rather than solely internal company missteps—holds true, it suggests the industry boom fueled by AI infrastructure is entering a new phase. However, the foundation for this growth is becoming narrower and more singular.

Ahead of the major tech companies' Q2 earnings season, IBM pre-released results showing its latest quarter's revenue grew by only 1%, significantly missing analyst expectations of around 5%. The company indicated that since last month, numerous clients have heavily redirected their standard IT budgets towards servers, storage, and memory devices, which are in high demand due to the AI boom. This shift has put pressure on sales of its mainframe and software businesses.

Cybersecurity concerns, such as those sparked by the release of Anthropic's "Mythos" AI model, have also disrupted corporate IT spending plans. IBM noted that companies are preoccupied with addressing security threats, forcing other digital procurement needs to be postponed.

The fact that a profit warning of a not-extreme magnitude caused a 25% plunge in IBM's stock price highlights the extreme fragility of investor sentiment. It also reflects market anxieties about a significant structural shift in IT demand.

Prior to this, there were few signs that AI was crowding out spending on other IT categories. The "SaaS crash" observed in the software sector stemmed more from market anticipation that AI would eventually reduce demand for existing software, rather than from any immediate, substantial impact.

Following Q1 of this year, witnessing the surge in data center spending, research firm Gartner directly revised its global IT spending forecast for 2026 upwards. The prevailing market view had been that AI would add to overall tech demand incrementally, not cannibalize other traditional IT investments.

However, IBM's warning signal suggests the industry landscape may already be changing. As the new earnings season begins, Wall Street has two core questions to answer: First, to what extent is the infrastructure boom crowding out other routine IT procurement budgets? Second, will the persistently rising costs of this infrastructure push up the pricing of AI services, thereby dampening related demand?

Spurred by the negative news from IBM, investors also sold off stocks of software firms like ServiceNow and Adobe, searching for other potentially affected targets. Electronics manufacturers may face subsequent impacts as well—rising component costs for products like PCs, gaming consoles, and smartphones could weaken the market appeal of these end devices.

Simultaneously, for companies that previously rode the wave of generative AI's rapid development, IBM's warning reveals a new risk amid intensifying resource competition. Krishna stated that clients are scrambling to allocate more budget for AI hardware to avoid cost pressures from rising prices of components like memory chips. This phenomenon highlights two potential major risks.

First Major Risk: Mismatched procurement timing could further increase the uncertainty surrounding AI infrastructure spending. To hedge against price hikes, companies might purchase hardware in large volumes before their supporting data centers are even completed. Once this stockpile is absorbed, future equipment spending could contract sharply. The market is currently highly sensitive to any signal of weakening AI capital expenditure, and such procurement cycle fluctuations could amplify valuation volatility across the sector.

Second Major Risk: Rising component costs will continuously squeeze corporate capital expenditure budgets. If the massive influx of funds into data centers largely flows to memory chip manufacturers rather than converting into new computing power supply, the overall expansion of AI production capacity will be constrained.

Nvidia's 75% gross margin is sufficient evidence of the rich profits chipmakers are earning; Micron's latest quarter gross margin of 83%, more than double the level from a year ago, further corroborates this trend.

Performance improvements in next-generation AI accelerator chips and High Bandwidth Memory (HBM) can partially offset the cost pressures from price increases. The cost of generating tokens—the fundamental output unit for large language models—is also expected to continue declining. However, the fierce competition for computing resources could push up the pricing of AI services. At a time when demand for various AI applications is rapidly exploding, such price increases would directly suppress market demand.