In the latest chief economist outlook, the focus shifts to the intricate dynamics of US Treasury yields, which are currently under pressure from a confluence of fiscal deficits, AI-driven industrial transformation, and shifting global capital flows.

While the Chinese economy shows a clear picture of structural pressure amid overall weakness, the global landscape is markedly different, with the "USD-US Treasury-US Equity" framework setting an upper limit for Treasury yields. However, in the cross-temporal choices of global asset allocation, even minor fluctuations in the risk-free rate can trigger tremors across the entire asset system. As global AI industry competition intensifies, the divergent paths of Chinese and US benchmark yields are steering the direction of worldwide capital. Understanding and leveraging this cross-temporal selection to maximize value discovery remains a core objective for asset allocators.

Market Review: External Strength, Internal Weakness

Monthly and weekly data reveal a clear pattern of external strength and internal weakness across major asset classes. Overseas precious metals led gains, with COMEX silver and London gold posting strong advances, while industrial metals like coking coal and LME copper also saw positive returns. This was driven by rising global risk aversion, pushing funds into precious metals as a hedge, with some industrial commodities benefiting from improved overseas demand expectations.

US and Hong Kong equity markets generally rose, supported by expectations of loose liquidity that lifted overseas valuations. Domestically, assets showed divergence: rate bonds performed steadily with minimal volatility, but major A-share indices weakened significantly. The STAR 50 fell the most, with the SSE 50 and CSI 300 also declining, reflecting subdued domestic risk appetite and cautious expectations regarding the pace of economic recovery and corporate earnings improvements. Commodities were mixed, with crude oil and agricultural products strengthening while the black series weakened.

Within A-shares, on a weekly basis, petroleum, petrochemicals, non-ferrous metals, and banking led gains, with pro-cyclical and defensive sectors outperforming. Media, computers, and defense stocks fell sharply, as high-volatility growth sectors corrected and capital rotated from them into more resilient resources and financials. On a monthly basis, sectors like comprehensive services, textiles, and social services gained, while utilities, non-bank financials, and communications declined. Consumption and some cyclical sectors showed repair momentum, but financials and communications were weak. This comparison highlights a market leaning toward risk-off behavior weekly, while monthly data shows resilience in selective consumption sectors. The core reason is weak expectations for economic recovery, prompting investors to favor low-valuation, defensive assets over high-growth ones, with financials pressured by fundamental outlook concerns.

US Fiscal Deficit Convergence Hinges on AI and Tax Reform; Yields to Stay Elevated Short-Term

In asset allocation theory, Treasury yields are typically the benchmark for the risk-free rate, determined by bond supply and demand, which in turn are influenced by inflation fluctuations and central bank policy rate adjustments. Investing in Treasuries is a choice between risk and risk-free assets, fundamentally a contest between private and public capital, weighing bond yields against returns on industrial capital. This choice assumes the issuer won't default; otherwise, Treasuries become the riskiest assets. Risk is inherent in capital structure, as capital is used for production, and time is needed from production to consumption, with varying degrees of capital roundaboutness.

For simplicity, capital can be divided into higher-order and lower-order types, which isn't just about duration but the complexity of production processes. Not all capital can complete the full production-to-consumption cycle, implying higher-order capital carries greater risk. From a pure public goods perspective, governments don't participate in production or consumption, so public capital behind Treasuries is essentially zero-order, with its self-contained nature seen as risk-free. Investors' participation in different capital games is determined by their risk-return trade-offs over time. Competing entities vying for investor funds create substitution effects, where expected returns reflect the urgency of their capital needs.

Applying this to the "US Equity-US Dollar-US Treasury" framework yields two conclusions. First, Treasury yield levels are directly linked to US equity returns and dollar strength. US equities reflect expectations of AI-driven growth, increased tax revenue, and reduced fiscal pressure, while Treasuries reflect growing interest payments and debt expansion. The dollar mirrors international investors' trust in US government financing capabilities. Second, the interplay of the AI revolution, debt expansion, and a strong dollar complicates macro analysis and asset allocation. Currently, the industrial revolution sees costs exceeding output, private debt pressures push asset yields higher, public debt expansion forces higher Treasury yields, and the dollar acts as a buffer. This implies that until US industrial transformation succeeds, Treasury yields are unlikely to decline systematically.

This conclusion is supported by a Yale Budget Lab macro-micro simulation model. Its quantitative findings show that even in the most optimistic rapid AI growth scenario, with real GDP growth exceeding expectations at 3.3%, the additional tax revenue AI could generate for the US government by 2030 would only reach $216 billion annually. This is a 3.3% improvement over a no-AI baseline, covering only 11.7% of the current annualized fiscal deficit and insufficient to offset the yearly increase in interest payments, which are expected to expand by over $100 billion annually. To narrow the fiscal deficit to a sustainable 3%, meeting debt dynamics convergence conditions without cutting welfare spending, AI would need to generate an additional $600 billion to $800 billion in tax revenue each year. This necessitates tax reform, such as introducing an "AI asset tax" or raising effective corporate tax rates on major tech companies.

Calculating the Short-Term Risk of a US Treasury Yield Meltdown

The twin deficits—trade and fiscal—have long been a feature of the US economy, especially during transition periods when new industries face a J-curve effect of initial costs before output. The US Treasury and Federal Reserve primarily work to prevent a yield meltdown through tools like bond buybacks, rollovers, or rate cuts to reduce interest costs. Moderate inflation also helps lower real debt burdens, alongside measures like fiscal discipline and tariffs on trade partners. These operations aim to wait for technological success, but until then, they affect the pacing of asset allocation without changing its overall trend.

It's crucial to recognize that the AI revolution hasn't eliminated US debt and credit risks; it merely allows the US to attract global capital to sustain large deficits through computing monopoly and tech assets. AI is the best "leverage buffer" for the US credit system before a potential collapse. If global capital realizes that AI capital expenditure returns can't keep pace with Treasury interest rollover, or if the offshore dollar collateral chain breaks under an external shock, liquidity crises could transmit more violently than in past tech revolutions. From an investment perspective, considering the epochal significance of AI is important, but it's an outcome; observing the process holds more practical value. Therefore, attention must focus on the risk of a Treasury yield meltdown, marked by the degree of failed Treasury auctions. This can be analyzed through a core macro-financial model: the Fiscal-Market Dual-Loop Dynamics.

1. Variable Selection

The model uses variables from fiscal, capital, and liquidity sides. Fiscal side: US Treasury yield (YG), healthcare and social security growth rate (GE), and overnight rate (RFF), determining government debt and interest out-of-control speed. Capital side: corporate bond yield (YC), AI company ROE and tax ROE (ROEAI/TAI), determining the crowding-out and supplementary capacity of AI and the real economy on Treasury funds. Liquidity side: MOVE index, determining when primary dealers and hedge funds trigger forced liquidation.

2. Core Equations

The model calculates three key indicators. First, the Fiscal Stress Ratio (FSR), determining when government finances enter a phase of borrowing to pay interest. Narrow FSR at or above 30% signals to markets that fiscal control is lost, causing irreversible spikes in term premiums. Second, the Credit Sensitivity Measure (CSM), determining whether capital flows to AI entities or forces Treasury yield breaks. When CSM is negative, AI asset returns fall below debt financing costs, leading to an AI capital expenditure collapse and breaking AI tax promises, removing the largest backing expectation for Treasuries. Empirical values suggest that when YC-YG is less than 1.0 and YG exceeds 5%, high Treasury yields strongly crowd out AI financing. Third, the Liquidity Stress Index (LSI), determining the precise micro-moment of a financial crisis. Historically, when LSI exceeds 140, repo market haircuts are forced up, basis trade leverage disappears, and a liquidity crisis erupts.

3. Latest Scenario

Using data from August 24, 2026, the overnight rate is 3.63%, the 10-year Treasury yield is 4.70%, and the 30-year yield is 5.23%. Annual interest payments are $1.85 trillion, with total fiscal tax revenue at $4.98 trillion. The MOVE index is 71.9, investment-grade corporate bond yields are 6.25%, and total US debt stands at $40.047 trillion. It's worth noting that as of August 2026, annualized net interest payments are about $1.05 trillion; the $1.85 trillion figure represents the theoretical annual interest payments after all low-rate old debt matures and is repriced under current high rates.

The calculations yield: 1) Narrow FSR equals 1.85/4.98, or 37.1%. 2) CSM equals 14.5% minus 6.25%, or 8.25%. 3) LSI equals 71.9 times (4.70%/3.63%), or 93.07. The key conclusions are: first, even with two Fed rate cuts in 2027, US debt will exceed $41.5 trillion, entering the phase of borrowing to pay interest, which will become a medium-to-long-term sword of Damocles for global capital markets in 2027. Second, in the short term, the probability of a sudden liquidity freeze in 2026 is extremely low. Third, the AI capital crowding-out spread of 8.25% is well above the 3.0% warning line; capital hasn't left AI, and the double-kill risk of an AI bubble burst forcing corporate debt defaults is unlikely short-term.

4. Historical Scenarios

Reviewing the past 20 years of these indicators: The FSR curve shows a one-way surge. From a healthy 60%-65% in 2006-2007, it first exceeded 100% during the 2020 pandemic relief. From 2022-2026, US debt grew from $23 trillion to over $40 trillion, with high-rate rollovers pushing the broad FSR to a record 121.03% danger zone. The CSM curve has been in a safe, ample zone. A brief dip to 2.5% occurred during the 2008 credit crunch. From 2010-2021, the mobile internet bull market kept CSM high at 8%-12%. In 2026, strong AI ROE easily covers corporate bond costs, keeping CSM at 8.25%, indicating low short-term double-kill risk. The LSI curve spiked above 220 during the 2008 financial crisis, well beyond the 140 warning line, triggering an acute liquidity crisis. In 2020, it briefly hit 170 before the Fed's unlimited QE pushed it back. In 2026, with the Fed cutting rates to 3.63%, the LSI has fallen to a safe 93.07.

Investment Strategy: Offensive on AI Returns, Defensive on Ponzi Tail Risks

Currently, US Treasury yields are unlikely to decline systematically in the short term. While an acute liquidity crisis hasn't erupted in 2026, the US will enter a phase of borrowing to pay interest by 2027. Fiscal deficit convergence heavily depends on AI asset tax reform; without it, long-end Treasury valuation pressures persist. The greatest current returns come from AI. The CSM of 8.25% confirms AI's ROE far exceeds debt costs, making it the only asset with endogenous growth alpha in a high-rate era. It should be the first core position for excess returns. AI remains the main track, but internal differentiation is significant, so a focus on profitability is key. Gold's pricing has changed; high FSR implies a significant possibility of ultimate credit loss for US Treasuries. Gold, as the ultimate CDS hedging sovereign Ponzi cycles, has long-term allocation value, but over-allocation sacrifices AI's high returns, so pacing is crucial. Investors should abandon hopes for long-duration Treasury capital gains, focus on short-to-medium duration coupon income, and pair with value sectors to hedge fiscal risks. For A-shares, the focus should be on domestic fundamentals repair, waiting for the right moment to act; timing control is key. With the LSI at 93.07 in a safe zone, close monitoring of FSR, CSM, LSI, and US tax reform progress is essential. Before a new round of inflation or credit crisis is triggered by the US fiscal crisis, it's prudent to realize AI gains, executing a precise switch from "tech arbitrage" to "defensive hedging," earning structural returns while reserving protection against tail risks. Key risk points for macro strategy include a rapid slowdown in AI profit realization causing equity-bond double kills, a sharp spike in Treasury term premiums, and liquidity crises from offshore dollar collateral chain shocks.