The AI buildout is in full swing, and it’s incredibly expensive.
Between Amazon, Alphabet, Microsoft, and Meta, over $720 billion of capital expenditures is projected to be spent this year, most of it on AI infrastructure, according to analyst estimates compiled by Bloomberg. Alphabet President and Chief Investment Officer Ruth Porat confirmed to me this week at the Fortune Most Powerful Women Summit that Alphabet intends to spend “significantly more” in 2027.
This year, Alphabet said it expects capital expenditures of $195 billion to $205 billion. That’s more than double the $91.4 billion it spent in 2025. When asked whether spending on the buildout has peaked across the industry, Porat said it is still in its early phases relative to the eventual economic value Alphabet thinks AI can drive. “We do see this as an opportunity that extends for quite some time,” she said.
Increasingly, debt is fueling the AI buildout too. Private credit funds such as Blue Owl, Apollo, and KKR are a growing source: Outstanding private loans to AI-related companies have climbed to more than $200 billion by some estimates. Often, they’re lending to special purpose vehicles and smaller “neocloud” operators rather than to deep-pocketed Big Tech players. Even Alphabet posted negative free cash flow in Q2 for the first time since its 2004 IPO to help fund AI. Its long-term debt was about $98 billion at the end of June, up more than four times the year prior.
Alphabet can afford such costly long-term AI bets—and arguably the bigger risk for it would be not making them. But many less well-capitalized players are vulnerable, and recent shifts may be making them more so.
One of the biggest: Open-weight models, particularly from China, are increasingly taking business and usage from closed models like Anthropic, OpenAI, and Google’s Gemini. New financial figures from Anthropic this week also suggest its growth may have flattened.
This trend led Jefferies’ global head of equity strategy, Chris Wood, to declare that the most likely long-term outcome of the AI boom is massive capital destruction in the U.S., according to Bloomberg. Wood argued that cheaper Chinese open-source models threaten to commoditize large language models, eroding the economics behind America’s AI spending, and that China “has become a technological peer to the U.S. in AI.”
When I recently spoke with one of China’s leading open-weight model developers, Moonshot AI, I learned that 70% of its usage comes from outside China.
I asked Porat directly about Wood’s thesis that much of the money pouring into the U.S. AI race could be destroyed.
“It’s illogical to say we’re in a capacity-constrained position because there’s such strong demand for the insight in the ability to improve business and government, and yet you can’t drive a return on it,” Porat replied. She also noted that when it comes to the trend toward open-weight models versus closed, she feels AI companies that add differentiated value will find customers paying for their services regardless.
Porat has had a front-row seat to previous bubbles, first as head of Morgan Stanley’s technology banking team during the dot-com era, and later during the 2008 financial crisis, when she helped advise then U.S. Treasury Secretary Hank Paulson. She went on to become Morgan Stanley’s CFO in 2010.
Her takeaway from those periods is that companies using AI to build truly differentiated experiences will win.
“One of the key lessons from the financial crisis was that you should identify your greatest source of vulnerability early and fortify against it, because you can’t address it in the moment. For banks, it was liquidity. Without liquidity, you couldn’t run a trading operation. Without liquidity, you shut down,” Porat said.
“I’ve been asked that question frequently since: What is the greatest source of vulnerability today? I think for all of us across industry, one of the most important elements is innovation in your business. If anyone is connecting with your customers in ways you are not, because they are using AI and you are not, you’re at a competitive disadvantage.”
Yes, she’s talking her book here, and the “just add more AI” view doesn’t negate the potential systemic economic risk of overbuilding. But it’s also true that AI is not going away and the need for companies to adapt is only going to increase.
At Alphabet, Porat indicated that the company will keep investing heavily in AI because of its conviction in the long-term economic upside.
“You must invest for long term growth,” Porat said. “By the time you need it, it’s too late to say, OK, now we’re going to start.”
For more, check out my full conversation with Google CIO and President Ruth Porat here.
