Recent surges in long-dated US Treasury yields to multi-decade highs, combined with mounting concerns over whether hyperscale cloud providers' AI-generated revenue can cover escalating AI compute capital expenditures, have reignited pessimistic bear market calls about an imminent AI bubble burst.
The debate among global institutional and retail investors over whether the AI bubble is about to burst has shifted from valuation levels of AI compute leaders like Nvidia and AMD to a more damaging substantive question: as long-term funding costs and long-duration financing expenses become increasingly expensive, can AI application-related industries—including NeoClouds, large model developers, and traditional cloud computing giants—support still-accelerating AI compute investments with real, sustained cash returns?
According to Joachim Klement, senior market strategist at London-based Panmure Liberum, the S&P 500 index target by end-2027 is set at just 5,000 points, implying approximately 36% downside—meaning this veteran strategist predicts US equities will fall into a deep bear market, and he forecasts the AI trading boom could unravel in 2027 or 2028.
Klement has even advised clients to formulate contingency plans and timing tools to prepare for a crash, and to adopt full defensive positioning when the S&P 500 drops below its 200-day moving average.
The strategist's concerns center on hyperscale cloud providers' AI capital expenditures continuously devouring free cash flow, and rising debt financing costs amid surging 10-year and longer US Treasury yields.
His views echo cautious AI infrastructure investment perspectives from Bloomberg Intelligence, which estimates data center capital expenditures could reach $713 billion in 2026, more than doubling year-over-year—meaning AI investment scale continues to accelerate, but financing is getting more expensive, and whether cash returns can keep pace remains unverified, creating a potential contradiction between expansion needs and financial capacity.
This means that as AI data center capital expenditures continue to expand, companies face dual tests of rising financing costs and investment return realization, with the former pressure increasingly escalating.
Some Strategists Begin Preparing for Bear Market! From 8,300 to 5,000: AI Bubble Burst Could Trigger Worst S&P 500 Plunge Since 2008
A market strategy head at a large London-based investment bank has issued a stern warning to investors: the artificial intelligence investment boom may soon end and could trigger the worst global stock market crash since the 2008 global financial crisis.
This year, global equities have surged strongly and repeatedly hit new highs driven by the AI super bull market around AI compute themes, partly fueled by investors' continued optimism about surging AI infrastructure spending.
But Joachim Klement from Panmure Liberum says his base case is that this AI trading boom will collapse and unravel as early as 2027, causing a sharp stock market decline. "My core judgment is that the AI bubble will burst in 2027 or 2028, sometime in the next two years," Klement said in a media interview. He noted that hyperscale cloud computing companies' free cash flow has been largely depleted, while debt financing costs are rising rapidly in tandem with 10-year Treasury yields, becoming prohibitively high for these companies.
Klement's S&P 500 target for end-2027 is 5,000 points, implying 36% downside from current levels. Compared with seven other strategists tracked by Bloomberg Intelligence, his forecast is clearly the most pessimistic; others on average expect the index still has 14% upside. He also expects Europe's Stoxx 600 to fall to 430 points, more than 30% below current levels.
This strategist, who began his fund manager career at UBS over two decades ago, is now among the earliest veteran strategists predicting this bull market's end. As recently as mid-September, his main judgment was that the S&P 500 would reach 8,300 by end of next year. As shown in the chart above, Klement's scenario would send the US benchmark index to multi-year lows.
His comprehensive shift in view within a short period stems from concerns that stubborn inflation could push the Federal Reserve into a new rate hike cycle, and investment financing costs surging with inflation expectations could derail the AI-related infrastructure investment boom. This view echoes warnings this week from Temasek International's Chief Investment Officer Rohit Sipahimalani: a full reversal of the AI trading boom is a key risk facing global markets.
According to latest estimates from Bloomberg Intelligence researchers, hyperscale cloud companies' data center capital expenditures could reach $713 billion in 2026, more than doubling from last year. This figure is expected to increase further next year, though at a slower pace. This spending expansion has become an important support for many US tech companies' earnings forecasts.
"What's happening now is that people only focus on one thing, and only this one thing—earnings, especially the earnings trajectory of tech companies linked to AI compute," Klement emphasized in a media interview. "Whatever macro, credit, or other headwinds you raise, they'll explain them away with this narrative."
Citigroup strategists said this week that despite higher rates and persistent geopolitical risks, the extremely robust earnings growth trajectory based on the AI compute infrastructure boom in 2027 can still support further global stock market gains. Klement acknowledges his bearish call may be premature. For European Stoxx 600 performance through end-2026, he remains the most optimistic among strategists tracked by Bloomberg Intelligence, expecting about 10% further upside for the region's benchmark index already on a bull market trajectory.
"I'm starting to alert people now because I think certain things may happen in six to nine months," he said in the interview. The strategist did not advise clients to sell stocks now, but rather to develop contingency plans and build timing tools to help identify the start of a crash. As shown in the chart above, the S&P 500 last fell below its 200-day moving average in March. His primary advice is to "fully shift to defensive investment strategy" once the S&P 500 breaks below its 200-day moving average.
This technical indicator, which averages index closing prices, helps identify long-term market trends. In this scenario, he advocates allocating to sectors with strong long-term defensive characteristics, including healthcare, food, tobacco, and pharmaceutical stocks. "What I'm telling people is that now is the time to start preparing," Klement said. "Contingency plans should be made now for the deep bear market after the AI bubble bursts."
Long Bonds Sound Alarm: AI Super Bull Market Undergoing Capital Cost Stress Test
The stronger the corporate spending associated with the ongoing AI compute infrastructure buildout, the more impressive the current performance of core AI compute chip suppliers; but if end-market AI revenue-related commercial returns continue to lag, companies funding compute construction may also bear increasingly heavy cash flow pressure.
However, it must be emphasized that the aforementioned 5,000-point S&P 500 bearish target is Klement's personal base case, not Wall Street consensus; he even remains bullish on European stocks for the remainder of 2026, advocating advance preparation rather than immediate liquidation.
As of Beijing time October 8, 2026, the latest complete trading day reflected long-dated Treasury yield expansion, stock market high-level oscillation, and internal repricing within the global AI-related supply chain. On October 7, the Dow fell about 0.66%, while the S&P 500 and Nasdaq Composite both fell about 0.22%, having just hit record closing highs the previous trading day; the Philadelphia Semiconductor Index fell 1.15%. Individual stock performance was notably divergent: Nvidia fell about 0.7%, SpaceX dropped about 2.5%, while Micron shares rebounded about 4.1% against the trend after a short-term decline caused by Toshiba HDD expansion news.
In global government bond markets, the US 10-year Treasury yield—dubbed the "anchor of global asset pricing"—briefly surged to about 5.36% on October 7, setting another highest level since 2002. The US 30-year Treasury yield briefly rose to about 5.73%, remaining in its highest range since 2002 at about a 24-year high. The UK 30-year gilt yield hit its highest level since January 1998, hovering near 6.05%.
As of concentrated Asian trading hours on October 8, market-implied probability of a Fed rate hike in October was about 19%, but December hike probability remained about 80%, and the probability of continued hikes in early 2027 was also growing; increased expectations for a pause at the upcoming meeting do not mean tightening expectations for the year have disappeared. Long-term government bond yields can be understood as "average expected future short-term rates plus term premium," the latter compensating investors for bearing long-term interest rate, inflation, and duration uncertainty. Therefore, even if Fed rate hike probability around October monetary policy declines, escalating geopolitical conflicts driving energy inflation shocks, massive government financing needs, and private debt issuance led by Microsoft, Google, Oracle, SoftBank and other AI tech issuers competing for long-term capital pools could all continue to push long-end yields higher.
The common pressure on the US, UK, and Japan comes from inflation and bond supply, with each having different amplifiers: the US faces simultaneous expansion of fiscal financing and AI financing; UK investors remain vigilant about government borrowing scale and budget arrangements; Japan adds central bank rate hikes, reduced bond purchases, and fiscal expansion expectations. Rising Japanese domestic bond yields could also increase opportunity costs for holding overseas long bonds, but this cannot be directly construed as large-scale US Treasury selling by Japanese institutions. What needs closer attention now is the rising long-term government bond returns demanded by global long-term capital pools, not just whether the next central bank meeting will hike rates.
Energy and geopolitics are extending this stress test. After US-Israel attacks on Iran on February 28, the April 8 ceasefire did not completely end hostilities; recent diplomacy remains constrained by nuclear negotiations, blockade lifting, and sanctions arrangement sequencing, while Iran simultaneously prepares stronger responses for possible renewed large-scale US military attacks. Entering October, risks of ship attacks in the Gulf and Strait of Hormuz have risen again; on October 7, an oil tanker near Qatar was attacked with casualties, continuing to impact transport security and insurance costs.
For the AI trading boom, the most important transmission path from persistently high oil prices near $100 is energy shocks raising inflation stickiness, compressing central bank easing space, and thereby maintaining higher real financing costs and risk compensation requirements. As of 12:27 Beijing time October 8, international crude benchmark Brent futures traded at about $102.28 per barrel, WTI at about $89.94 per barrel; based on settlement prices of $72.48 and $67.02 respectively on February 27, the last trading day before the war, cumulative gains were about 41.1% and 34.2%. These are front-month contract price comparisons.
The International Energy Agency on October 7 supported accelerating implementation of the reserve release plan announced in March, prioritizing diesel stock releases, which had helped oil prices retreat; but reserve releases can only buffer supply pressure, not completely eliminate oil and gas supply crises and energy inflation under shipping attack and conflict escalation risks.
The Real AI Trading Kill Switch: When Compute Expansion Speed Continues to Exceed Cash Return Capacity
The core disagreement in this market debate about "when the AI bubble will burst," as shown by London-based Panmure Liberum senior strategist Joachim Klement's core view, lies in whether a real technological revolution can support current investment scale, financing structure, and asset prices.
Undoubtedly, the super bull market dominated and driven by AI compute is entering a significant differentiation phase where "earnings realization and financing capacity jointly determine victory." Galaxy Digital founder Mike Novogratz believes the bubble has not yet entered its final frenzy stage, so there is still room to participate; Bridgewater founder Ray Dalio focuses on interest rates, US Treasury yield trajectories, and liquidity pressure when investors convert paper wealth into cash; "Black Swan" author Nassim Taleb points risk toward bond market absorption capacity, reminding investors that technology changing the world does not guarantee early leading companies' shareholders will receive ideal returns.
In the view of top strategists at Wall Street financial giants like Goldman Sachs, strong earnings from compute suppliers like Nvidia and Micron reflect robust AI infrastructure demand, but whether downstream cloud providers and data center operators can convert massive compute investments into sustained cash returns sufficient to cover capital costs remains key to judging this investment boom's sustainability.
Goldman Sachs senior trader Tony Pasquariello cited an AI infrastructure stock sample whose median forward P/E fell from about 32x in April to 22x, reflecting that earnings growth has already digested some valuation; earnings contributions from Nvidia, Micron and others also support this rally having real fundamental basis. However, for GPU buyers, cloud providers, and data center operators, capital expenditures immediately form expenses on the cash flow statement while typically appearing gradually through depreciation over future years on the income statement, creating simultaneous profit growth and free cash flow pressure.
Pasquariello further noted that one company borrowing to buy chips can immediately support another company's revenue, yet has not proven the entire AI ecosystem has generated sufficient end-market paying revenue to repay these debts. Therefore, assessing the sustainability of global AI industry prosperity requires penetrating inter-company procurement and financing cycles to observe cash receipts from customers outside the ecosystem.
Another Goldman Sachs strategist Bobby Molavi's capital expenditure cycle research provides another important reminder: stocks price changes in future investment returns, while capital expenditures often reflect construction commitments already made in the past. Therefore, after stock prices peak, equipment deliveries, data center construction, and capital expenditures may continue rising for several quarters. Historically, similar lags of 6 to 24 months have occurred, but this is not a timing formula that can be mechanically used to predict a 2027 top.
Meanwhile, Molavi's calculations showing AI-related thematic stocks account for about 42% of S&P 500 market capitalization under specific classification criteria reveal the index's high dependence on a few earnings engines; this can amplify upside from earnings upgrades and also amplify shocks from capex deceleration or valuation downgrades.
SpaceX, the space exploration and AI leader founded by Musk, recently announced a $40 billion financing plan that serves as an important window for observing AI investment shifting from equity narrative to credit market constraints under an "AI bubble burst" tone. According to media reports, the company plans to purchase Nvidia chips with about $10 billion in bank loans and $30 billion in investment-grade debt, with the transaction still in fundraising stage; meanwhile, its five-year CDS spread rose from about 110 basis points in June to 194 basis points, and its 2056-maturity bond spread relative to US Treasuries widened to about 236 basis points. These collectively reflect investors demanding higher risk compensation, not equivalent to a 194 basis point default probability.
When US Treasury benchmark yields and corporate credit spreads rise simultaneously, new debt costs face double squeeze; existing fixed-rate debt coupons will not change immediately, but new project financing, floating-rate loans, and maturing refinancing will come under pressure. Shortening issuance maturity can temporarily reduce some financing costs but may increase future refinancing frequency; borrowing in different currency markets also requires considering currency hedging costs.
Another Wall Street financial giant Deutsche Bank's latest revelation of "structural misalignment" focuses on five lines: sovereign debt, corporate credit, central bank policy, energy curve, and stock valuations. Deutsche Bank said the France-Germany 10-year bond spread widened 32 basis points in a single week, and the Italy-Germany spread widened 23 basis points, showing sovereign risk compensation rising rapidly; but stock and corporate credit spread adjustments were relatively limited over the same period, meaning investors have not fully priced in consequences of financing pressure transmitting to the real economy. Meanwhile, Deutsche Bank strategists said markets are betting central banks will pivot to easing due to financial turmoil but may underestimate high inflation's constraints on policy; oil forward curves still reflect pricing that supply pressure will ease, while spot supply risks linger.
Deutsche Bank strategists said what truly warrants vigilance is that stocks still rely on strong earnings to absorb macro shocks, while bonds already demand higher risk compensation. By October 7, European bank stocks had fallen significantly; Deutsche Bank said this sufficiently demonstrates such pressure beginning to transmit gradually to stock markets, and "stocks completely unmoved" no longer adequately describes the latest state.
From a data center and large model engineering economics perspective, what truly needs examination is how much distributable cash each unit of effective compute can generate over equipment economic life. GPU purchase price is only the starting point; data center power-on time, cluster utilization, HBM and data center optical interconnect constraints, inference scheduling efficiency, power costs, and customer collections all determine actual investment returns. Token usage growth is also not proportional revenue and profit growth: cache reuse, model compression, batch processing, and competitive price cuts may all reduce revenue per token; only if efficiency gains from cost savings can fully convert into new paying demand can investment returns continue improving.
Especially when US market power-on delays, accelerated equipment iteration, or high customer concentration occur, financing interest has already begun accruing while stable cash income may not arrive simultaneously. Once project risk-adjusted expected returns persistently fall below all-in capital costs, continued expansion may shift from creating value to consuming value.
Surging long-term yields may become an important catalyst for pricking the AI asset bubble, but there is no unified switch where "10-year Treasury reaches a certain level and AI necessarily crashes." If long-term real rates remain high, AI corporate credit spreads continue widening, and end-market commercialization or earnings expectations begin significant downward revisions, then continued capital expenditure increases will pressure buyer cash flow, while cutting capital expenditure will affect chip and equipment supplier orders, creating simultaneous pressure on both ends of the supply chain. Conversely, if financing markets remain open, energy shocks ease, and corporate payments and inference revenue continue to materialize, earnings growth may still offset higher discount rates, and the AI-driven global super bull market may also extend through sector rotation and valuation digestion.