Chris Wood warns of massive capital destruction in US as China challenges AI boom

Jefferies’ Global Head of Equity Strategy Chris Wood has warned that hundreds of billions of dollars being poured into AI infrastructure could culminate in “massive capital destruction” as cheaper Chinese open-source models erode the economics underpinning America’s investment frenzy.

Wood’s longer-term base case is that market share will shift towards Chinese large language models, while investors increasingly question whether US technology companies can generate adequate returns from their unprecedented capital expenditure.

Microsoft, Alphabet, Amazon and Meta are expected to spend a combined $695 billion on capital expenditure in 2026, rising to $870 billion in 2027, according to figures cited in Wood’s latest GREED & fear report. Together, that amounts to nearly $1.57 trillion over two years.


Alphabet raised its 2026 capital expenditure guidance by another $15 billion to between $195 billion and $205 billion. Investors will now focus on the guidance from Microsoft, Amazon and Meta as they report earnings.

The scale of spending has transformed businesses once known for their asset-light models. After raising their guidance in April, the four hyperscalers’ estimated capital expenditure reached an “astonishingly high” 92% of their forecast operating cash flow for 2026, according to Wood.

The market initially welcomed that spending, partly because surging revenue at AI companies appeared to validate demand. Anthropic’s annualised revenue run rate jumped from $9 billion in December to $47 billion in May, reinforcing optimism about corporate adoption and the monetisation of agentic AI.

But Wood said investors are now beginning to ask the question that had largely been deferred: where will the returns on this capital come from?

Also Read | Chris Wood’s big warning: The specific risk that will finally trigger the end of AI trade

China challenges US dominance

The threat from China is no longer confined to cheaper models with Wood saying that there is a growing realisation that China has become a technological peer to the US in artificial intelligence.

The top Chinese AI models processed 36.39 trillion tokens on OpenRouter during the week ended July 19, compared with 7.39 trillion tokens for the leading US models. Chinese models, therefore, handled nearly five times as many tokens on the global aggregation platform.

Competition intensified with the July 17 launch of Moonshot AI’s open-source Kimi K3. The model was estimated to offer about 95% of the performance of Anthropic’s Claude Fable 5, according to the data cited in the report.

The development builds on the “DeepSeek moment” of January 2025, which first brought the cost advantage of Chinese open-source models and the associated commoditisation threat to global investors’ attention.

Wood’s concern is that the continuing decline in token prices could prevent large language models from becoming sustainably profitable. The Silicon Data LLM Token Expenditure Index, which tracks the average price paid for one million AI tokens, has fallen 25% since its late-May peak to $1.55.

Falling prices may stimulate long-term demand for computing power, but they also threaten the profitability assumptions behind the current investment cycle.

Also Read | Christopher Wood warns of AI fatigue. Why Jefferies is turning to India and China

AI boom acquires a credit dimension

The risk is no longer confined to equity valuations. AI infrastructure spending has increasingly been financed with debt rather than the hyperscalers’ cash, giving the boom a growing credit-market dimension.

The leading hyperscalers have raised $194 billion through investment-grade debt in 2026, making them the largest single source of issuance and putting them well ahead of the US energy sector’s $55 billion.

Credit markets are beginning to show signs of concern. The spreads on 10-year bonds issued by Amazon, Alphabet and Meta have widened to 78, 70 and 104 basis points over US Treasuries, respectively, from 61, 57 and 87 basis points on July 3.

Oracle, a more leveraged participant in the AI infrastructure race, had its $120 billion debt pile downgraded to BBB- on July 9, leaving it one notch above junk status. Its 10-year bond spread has widened from 176 basis points to 219 basis points since the downgrade.

The larger vulnerability lies in the revenue backlogs being used to justify infrastructure investment.

Microsoft, Alphabet, Amazon and Oracle had about $2.1 trillion of remaining performance obligations at the end of the first quarter of 2026. These represent contractual commitments for future revenue and have surged 184% from $740 billion a year earlier.

About half of that backlog is owed by OpenAI and Anthropic, according to figures cited by Wood. Microsoft’s backlog has about 49% exposure to the two AI companies, while the corresponding exposure is 54% for Oracle, 43% for Google and 51% for Amazon.

Neither OpenAI nor Anthropic is currently profitable, although Wood said Anthropic appears more comfortably positioned. The concentration means hyperscalers have effectively extended large, unsecured commitments to cash-burning customers while building data-centre capacity on the assumption that future computing demand will materialise.

The risks are even greater for specialised cloud providers that have themselves borrowed to finance chips and infrastructure. CoreWeave has borrowed about $30 billion, while its five-year credit-default-swap spread has climbed from 452 basis points in early June to 701 basis points.

Hidden liabilities and flattering earnings

The balance-sheet risks may also be understated. Wood cited an estimate that the five leading US hyperscalers had accumulated $662 billion of future data-centre lease commitments that had not yet commenced, up from $152 billion at the end of 2023.

A separate study put their off-balance-sheet or “hidden” debt at $1.65 trillion in the latest quarter, exceeding the roughly $1.35 trillion of debt reported on their balance sheets.

At the same time, the accounting impact of the investment wave has yet to catch up fully with the expenditure. Microsoft, Amazon, Alphabet and Meta collectively spent $130 billion on capital expenditure in the first quarter, while recording depreciation and amortisation expenses of $41.6 billion. Depreciation was nevertheless 33% higher from a year earlier.

Wood also flagged what he described as financial engineering in the hyperscalers’ recent profit growth. Their annualised earnings increased by $106.6 billion from a year earlier to $447 billion in the four quarters through March. Other non-operating income increased by $71.5 billion to $83 billion, accounting for about two-thirds of the earnings increase.

The recent earnings strength could therefore be obscuring the scale of the risks accumulating beneath the AI trade.

Wood stressed that AI is not a passing story and that falling computing costs could ultimately drive much greater usage. His warning is about timing: markets may have overestimated the technology’s near-term returns while underestimating the capital and credit risks required to reach its long-term potential.

“The time for an extended AI hangover after the initial surge of enthusiasm is approaching, if it has not already arrived,” Wood said.

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