A financial analyst is questioning whether the collapse in AI costs owing to the arrival of Chinese, low-cost and open source models will cause a “reset” in the AI story as cloud providers struggle to recoup trillions of dollars of capital spending.
In a recent research note focused on Asian markets, Jefferies estimates that about half of the roughly $2.3 trillion contracted backlog at Microsoft, Alphabet, Amazon and Oracle is owned by OpenAI and Anthropic. Yet Chinese AI models, open source/weight and small language models, threaten to undercut the prices the two US leaders charge, casting doubt on their future revenue.
Jefferies points to research from global aggregator platform OpenRouter, which found Chinese models processed 76.12 trillion tokens in the week ended 27 September, up from just 4.37 trillion in late April. The top four US providers processed 14.2 trillion tokens. Meanwhile, the launch of Moonshot AI’s open source Kimi K3 is “competing at the frontier” with an estimated 95 percent of the performance of Anthropic’s Claude Fable 5.
“The question now is whether all of the above has been priced fully into markets. This writer does not think so,” the Jefferies paper states.
The largest hyperscalers are set to collectively spend between $600 and $800 billion this year on building datacenters, funding it increasingly with debt rather than cash. Jefferies, however, questions where the hyperscalers' future revenue streams will come from.
“It can no longer be assumed, most particularly with the arrival of the likes of Kimi K3, that large language models will ever be profitable given the related ongoing collapse in token pricing. This is extremely relevant for the hyperscalers since they have invested in datacenter capacity on the assumption that the compute will be purchased by the likes of Anthropic and OpenAI,” the paper says.
While the long term bet on AI may be sound, in the medium term, there is a lot of uncertainty, it argues. “The AI story will be due for a big reset at some point in the not-so-distant future. Are the four hyperscalers really going to spend $993 billion on capex in 2027?” Jefferies questions.
There are also important questions to ask about how much of the hyperscalers’ reported profits are down to their investments in AI model builders on which they will also depend for revenue. “Other income and expenses” — which describes gains in investments — have spiked in importance. Jefferies says these investments are “likely to include positions in unlisted AI-related stocks.”
While the four major hyperscalers’ annualized earnings increased by $222 billion to reach $581 billion in the four quarters to 2Q26, “other non-operating income/loss” accounted for all of that increase, according to the analyst. Jefferies says “other non-operating income/loss” increased by $224 billion year-on-year to $237 billion over the same period, meaning it accounted "for all of the increase in earnings."
Other research has underlined how much revenue and borrowing cloud providers might need to fund their datacenter building plans. The 2026 annual Global Technology Report from management consultants Bain & Company says the AI industry must generate $6 trillion in annual revenue by 2031 to keep funding the infrastructure needed to meet anticipated demand. That means the industry will need new uses beyond employee productivity.
Bain suspects existing AI applications might make up $1.8 trillion in revenue, but this still leaves a $4.2 trillion shortfall. It could be plugged by emerging AI applications such as search engines and ads, AI-built autonomous vehicles, and AI simulations. Even if they work out, these new categories will only add up to $1.5 trillion in revenue, leaving a $2.7 trillion gap which might be filled by “new products and uses that don’t exist today.”
does not offer investment advice.®