Alphabet’s chief investment officer responds to Jefferies warning that the most likely outcome of the AI boom is massive U.S. capital destruction
Fortune Alyson Shontell ● Covered by 9 sources
Alphabet says AI spending isn’t peaking yet, even as Jefferies warns the boom could wipe out huge amounts of U.S. capital. Rising debt and open-weight rivals are making the race look a lot less safe.
Based on reporting by Fortune, Alyson Shontell — read the original for the full story.
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Alphabet’s Ruth Porat is not buying the idea that the AI buildout has already hit a ceiling. The company now expects capital expenditures of $195 billion to $205 billion this year, more than double the $91.4 billion it spent in 2025. Porat’s view is that the industry is still early in the cycle, at least relative to the economic value Alphabet thinks AI can eventually produce.
That confidence is showing up in the financing too. Private credit firms including Blue Owl, Apollo and KKR are increasingly feeding the buildout, with outstanding private loans to AI-related companies topping $200 billion by some estimates. A lot of that money is going to special purpose vehicles and smaller neocloud operators, not just the biggest players with huge balance sheets. Alphabet itself even posted negative free cash flow in Q2 for the first time since its 2004 IPO to help fund AI, and its long-term debt was about $98 billion at the end of June, more than four times the level from a year earlier.
For Alphabet, that kind of spending is survivable. For weaker players, it may not be. The pressure is coming from another direction too: open-weight models, especially from China, are taking usage and business away from closed models such as Anthropic, OpenAI and Google’s Gemini. New financial figures from Anthropic also suggest its growth may have flattened.
That mix is what pushed Jefferies’ global head of equity strategy, Chris Wood, to argue that the most likely long-term result of the AI boom is massive capital destruction in the U.S. His case is that cheaper Chinese open-source models could commoditize large language models and wreck the economics behind the spending spree, with China now a “technological peer” to the U.S. in AI. Porat’s answer was blunt: demand is still strong, companies that bring real differentiation will keep customers, and firms that fail to innovate will get left behind.
Her frame is familiar. Porat lived through the dot-com era and the 2008 crisis, and she took away a simple lesson: identify your weak point early and shore it up before the pressure hits. At Alphabet, the weak point is not spending too much on AI. It is spending too little and watching someone else own the customer relationship.
My take — AI-written commentary, not fact-checked reporting
This is classic big-tech logic: if you can afford the fuel, keep stepping on the accelerator and call it strategy. The uncomfortable part is that open-weight models may be turning the AI race from a moat-building exercise into a price war, and price wars eat balance sheets for breakfast. The U.S. may not be short on ambition here; it may be long on expensive optimism.
Read more about this at: Fortune
