Hyperscalers and Vendors Bankroll Trillion-Dollar AI Buildout, But Enterprise Users Will Foot the Bill

The AI spending boom has reached staggering proportions, with global investment projected to hit $2.52 trillion this year—a 44 percent increase from 2025's $1.76 trillion, according to Gartner. This represents a significant upward revision from the analyst firm's previous estimate of $1.5 trillion, and the trajectory shows no signs of slowing, with spending expected to nearly double again to $4.7 trillion by 2029.

However, the financial dynamics behind this spending spree reveal a critical shift in who bears the cost. According to John-David Lovelock, Distinguished VP Analyst at Gartner, cloud hyperscalers and software vendors are currently absorbing the bulk of these investment costs in the short term, while enterprise IT departments will ultimately have to pay the price in the long run.

The Infrastructure Race and Its Financial Burden

Hyperscalers are making unprecedented capital investments, purchasing "billions of dollars' worth of servers" to build what Gartner describes as "the foundation for the next super cycle of intelligence." This infrastructure race is driven by the explosive demand for AI-enabled devices and services, from consumer chat applications to enterprise AI agents.

Lovelock notes that while consumers are happily adopting AI-enabled mobile phones, PCs, and tablets, and creating content with AI tools, enterprise buyers—whether CIOs or board-appointed special groups—are demanding "Get me something AI." However, the reality on the ground is far more complex.

The Trough of Disillusionment

Enterprise users are currently experiencing what Gartner identifies as the "trough of disillusionment" with AI, with project failure rates reaching approximately 90 percent. This represents a critical inflection point in the technology adoption cycle.

"We're starting to see the end of the investment line. We had a thousand flowers blooming, now it's time to prune the garden," Lovelock explained. "We are getting to the point where we go from 'that was a great idea' to 'where's my revenue?' That's a normal part of any new technology."

This disillusionment phase is driving a fundamental shift in how enterprises approach AI implementation. Rather than attempting to build custom AI platforms using third-party technologies—a complex and high-risk endeavor—organizations are increasingly turning to their existing software vendors for integrated AI solutions.

The Vendor Strategy: Short-Term Losses for Long-Term Gains

Software vendors are adopting a two-pronged strategy: defensive positioning and long-term monetization. Companies like Salesforce have already integrated AI capabilities such as Einstein, ChatGPT, and Agentforce directly into their software portfolios.

This approach offers enterprises what Lovelock describes as "low-risk projects," particularly when AI features are included as part of software updates without additional cost. However, the long-term financial implications are significant.

Salesforce's Chief Revenue Officer Miguel Milano has been candid about the company's strategy, stating that the CRM giant is willing to lose money on fixed-price AI agent contracts in the short term. "I have another 20 years to monetize that customer," Milano said, highlighting the long-term revenue horizon that justifies current losses.

Lovelock encapsulates this approach as vendors being "happy to take short-term losses on AI investment because they would have plenty of opportunities to make the margins that their investors expect in the long term."

The Defensive Imperative

Beyond long-term revenue potential, vendors are making a defensive play. "If you're a Salesforce, you have to have AI in order to defend your CRM revenue," Lovelock noted. "This year, more than half of the money being spent on enterprise application software will be spent on applications with GenAI in them. If a vendor's product does not have GenAI, the market expectations for growth are negative. It's a collapsing market without AI."

This creates a market dynamic where AI integration is no longer optional but essential for survival. The financial burden of this integration is currently being borne by vendors and hyperscalers, but the economic model suggests this will eventually shift to end users through subscription increases, usage-based pricing, or new service tiers.

The Enterprise Reality: Complex Infrastructure Challenges

The article references several concerning trends that compound the financial pressure on enterprises. Over half of AI projects are being shelved due to complex infrastructure requirements, and there are growing concerns about security vulnerabilities in AI systems. Recent incidents include a "contagious Claude Code bug" that spread from Anthropic's systems to Cowork, and a CISO who reported red-teaming their own AI agent to run an infostealer on an employee laptop.

These challenges highlight why enterprises are increasingly relying on established vendors rather than building custom solutions. The complexity of AI infrastructure, combined with high failure rates, makes the vendor-provided AI path more attractive despite the long-term cost implications.

What Changes for Enterprises

The shift toward vendor-provided AI solutions represents a fundamental change in enterprise IT strategy. Rather than building custom AI platforms, organizations will:

1. Rely on incumbent providers for AI capabilities integrated into existing software
2. Accept subscription increases as vendors recoup their AI investments
3. Face reduced flexibility as AI becomes locked into vendor ecosystems
4. Experience accelerated adoption as AI features become standard in business software

The financial model is clear: hyperscalers and software vendors are funding the trillion-dollar AI buildout through massive capital investments and short-term losses. However, as the market matures and the technology becomes essential for business operations, these costs will inevitably be passed through to enterprise customers.

This represents a classic technology adoption pattern where early infrastructure investments are made by platform providers, with the costs gradually shifting to end users as the technology becomes indispensable. For enterprises, the challenge will be navigating this transition while managing budgets and ensuring they derive sufficient value from AI investments to justify the eventual price increases.

The AI spending spree shows no signs of slowing, but the financial burden is already being redistributed, setting the stage for a new era of enterprise software economics where AI capabilities are both essential and increasingly expensive.