The stock market is currently distancing itself from Big Tech.
Google recently experienced its worst trading day on record. Intel suffered its steepest decline since the Nixon era. Amazon and Meta also saw significant pullbacks this week. The Nasdaq has tumbled, leaving it just over one percentage point away from a correction, defined as a 10% drop from its all-time peak.
The common thread: Building the infrastructure to power AI is incredibly expensive—and Big Tech profits are not yet justifying that massive outlay. AI companies are increasingly spending more cash on data centers than their core businesses can generate.
However, if the stock market is attempting to send a message, Big Tech is not listening.
Spending on AI has reached $1.6 trillion since 2023 and is projected to hit $1 trillion annually for the next decade or so.
That is because the market financing this expansion—the bond market—is largely unbothered while the stock market experiences a mild panic. Lenders have been lining up to provide the debt Big Tech needs to fund the technology currently reshaping the global economy.
This suggests that the questions the market is asking about AI—will Big Tech continue to increase spending? (it most certainly will) and will AI turn a profit anytime soon? (it doesn’t look like it)—are the wrong ones.
“A ton” does not quite capture the scale. Those four hyperscalers alone have accounted for more than a third of all capital expenditures from the S&P 500 over the past 12 months, notes John Higgins, chief economic adviser at Capital Economics.
This is why one-third of Big Tech’s capital expenditures this year are expected to be financed with debt—roughly $250 billion worth of bonds from hyperscalers alone. That figure is projected to peak next year at approximately 35% of hyperscalers’ spending.
But bond market investors do not have infinite recularly from US Treasuries, as the government attempts to fund Social Security, Medicare, and an expensive war simultaneously
This raises an obvious question: Exactly how much money are investors willing to lend to AI companies?
It is more than you might think.
The hypothetical maximum amount of debt the market can absorb is unknowable, but Goldman Sachs performed the calculations and identified $510 billion of available investment-grade debt for the largest hyperscalers. That is more than double what they are currently expected to borrow by the end of the year.
In other words, the biggest AI companies still have plenty of runway left to borrow.
The question is not how much; the question is how much longer.
The answer depends on two factors: who is paying and who is receiving.
“We are not concerned about access to capital,” said Amanda Lynam, chief credit strategist for Goldman Sachs on a recent
And the advent of agentic AI has sent demand exploding: requests from agents consume 15 times as many tokens as human-initiated requests, according to
