Issue #11: AI is the World’s Most Expensive Experiment
Why capital, valuation, and debt now power the world’s biggest tech story.
Artificial intelligence has become the defining force of this decade. No one can doubt about that.
But behind every breakthrough lies a staggering bill, and a bigger story.
The Price of an AI Revolution
In just two years, the AI race has escaped from just “tech news” and evolved into a macroeconomic event.
The latest corporate earnings from Big Tech companies, paint a picture of an industry powered as much by capital markets as by innovation.
Meta, Microsoft, and Alphabet are pouring hundreds of billions into AI data centers and chips.
Nvidia, now the world’s most valuable company at $5 trillion, has become the ultimate winner of that spending spree.
And Oracle (alongside smaller AI infrastructure players) has begun borrowing heavily to stay competitive, echoing the leverage-driven patterns of the early 2000s tech boom.
All of these decisions, hint that AI’s growth is about who can afford to keep building, and that the spending race is on.
Big Tech’s Billion-Dollar Gamble
Mark Zuckerberg describes Meta’s position as “a compute-starved state,” while Satya Nadella calls this “the massive opportunity ahead.”
Meta raised its 2025 capital expenditure forecast to $70–72 billion, with even more aggressive spending expected in 2026. Alphabet increased its target to $91–93 billion, nearly double its 2024 outlay. And Microsoft spent $34.9 billion on data centers in a single quarter (up from $24 billion previously).
Some analysts are starting to wonder when AI spending crosses the line from bold investment to overspending.
According to Bank of America, AI infrastructure has become one of the two primary drivers of U.S. GDP growth, alongside consumer spending a sign that AI is now influencing national economic performance as much as technological progress.
The Center of Gravity
Once known for just a gaming GPUs manufacturer, and today is becoming a synonym of AI economy.
Nvidia has managed to be at the heartbeat of every major player that uses artificial intelligence, as they rely heavily on Nvidia’s chips.
“AI begins and ends with Nvidia,” says R. “Ray” Wang of Constellation Research.
The company’s $5 trillion valuation reflects the market’s conviction that AI demand will remain exponential. Yet it also exposes a fragile dependency.
If Nvidia faces production bottlenecks or demand normalizes, the entire ecosystem could feel the shock. That means Big Tech earnings to national growth projections.
The Debt Engine: Borrowing to Stay in the Game
While tech giants are spending their own cash, others are financing their ambitions on borrowed money.
Oracle recently signed a $300 billion, five-year deal with OpenAI to build AI data centers. A partnership likely requiring $25 billion in annual borrowing for several years.
The company’s debt-to-equity ratio now exceeds 450%, one of the highest among major U.S. firms.
Smaller players like CoreWeave and Nebius Group are following similar paths, using debt to fund infrastructure that may take years to monetize.
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Credit agencies are already sounding alarms. Moody’s warned of “significant risks” tied to Oracle’s obligations and “an extremely large growth in balance sheet exposure.”
As analyst Gil Luria put it:
“A vast majority of Oracle’s data center capacity is now promised to one customer, OpenAI, who itself does not have the capital to afford its many obligations”
And that’s how economical bubbles form, with optimism funded by leverage.
The Fault Lines
Across these stories, three major vulnerabilities emerge.
The first is concentration. The entire AI ecosystem depends on a small number of suppliers. Nvidia, Microsoft, and OpenAI among them. This creates supply-chain chokepoints and exposes the industry to fragility. If one of these players faces disruption, the ripple effects could stall the broader AI economy.
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The second is leverage. Debt is increasingly replacing cash as the fuel for AI’s next investment wave. Companies like Oracle, CoreWeave, and Nebius are borrowing heavily to finance infrastructure that may take years to generate returns. The result is rising credit exposure and mounting financial risk across the sector.
Finally, there’s the adoption gap. Despite all the investment and hype, only about 3% of consumers currently pay for AI services. This mismatch between massive infrastructure spending and modest real-world demand raises the question of sustainability.
And we end up with a paradox.
AI dominates public conversation, yet the economic adoption curve remains surprisingly shallow.
Are We Financing a Mirage?
Every technological boom creates its own mythology. For the internet, it was eyeballs, visitors on the websites. For AI, it’s compute.
The vast infrastructure being built today with data centers, chips, and cloud capacity could define the next decade of innovation. But it could just as easily collapse under its own financial weight if growth slows or returns fail to materialize.
Companies should and ought to ensure that their investments in AI will deliver measurable returns before the credit that fuels this boom begins to run dry.
The key…
AI can do a lot of things (with mistakes - don’t forget this).
What companies must do now, is to invest wisely on artificial intelligence.
No matter how many euros or dollars one company, fund or individual pours in this technology, they won’t see any return if they make sure that they get clear results on their productivity and efficiency.
What we must see next is the era of AI accountability, with success being a result of discipline.