Why have U.S. AI tech stocks—such as AMD—experienced such sharp volatility recently, while A-share and Korean tech sectors appear comparatively subdued?

Actually, I’d say this isn’t entirely an illusion—the core issue is that market pricing anchors have clearly diverged, and the underlying market sentiment has been highly speculative and tense lately. I. U.S. chip giants have effectively become America’s ‘macro assets’ First, consider the U.S. market: AMD’s share price surged roughly 185% year-to-date, while the Nasdaq rose only about 15.8% over the same period. On September 21, AMD jumped 9.6% in a single day, pushing its market cap above $1 trillion; Intel rose 12.1%, and the Philadelphia Semiconductor Index gained 4.3%. Yet just one week earlier—on September 14—after AI executives signaled a potential slowdown in AI development, the Philadelphia Semiconductor Index plunged 5.2%, and AMD fell 4.5%. This volatility has little to do with fundamentals—it’s driven purely by expectations and sentiment, amplified by layered options positions and quantitative trading strategies. Today, both AMD and Intel are effectively priced across three distinct markets simultaneously: demand for AI compute power, U.S. long-term interest rates (and GDP growth expectations), and global risk appetite. Meanwhile, legacy leader NVIDIA—already prohibitively expensive and massive in market cap—has become too unwieldy for aggressive speculation; thus, investors have shifted their sentiment premium to AMD, Intel, and other names. AI capital expenditures represent an extremely long-duration cash-flow story: investors aren’t buying next quarter’s earnings—they’re betting on multi-year expansion of data centers, AI servers, and CPU/GPU demand. Therefore, when the 10-year U.S. Treasury yield rises—and discount rates increase—highly valued tech stocks inevitably suffer. Conversely, when oil prices fall and long-end Treasury yields decline, markets regain confidence in sustained AI capex, and valuations rebound swiftly. Earlier, Goldman Sachs pointed out that U.S. AI investment—and large tech firms’ capex expectations—have already reached extremely high levels, and large tech companies’ debt financing is transmitting through bond markets into the broader asset-pricing system. This means AI infrastructure stocks’ volatility is now tightly linked to earnings expectations, financing costs, and options-market pricing. In short, U.S. AI leaders are transitioning from ‘technology company stocks’ into macro assets with pronounced duration characteristics—and the driving force behind this shift is essentially Trump’s ‘can’t-lose’ mindset! Yet U.S. finance is highly mature: markets are markets, and rhetoric is rhetoric. Markets themselves possess strong self-correcting momentum. II. Why does the A-share tech sector appear less ‘sensitive’? In the past, we often perceived A-share tech stocks as leverage amplifiers—trading at P/E ratios in the thousands. But let’s be clear: A-share tech valuations cannot anchor to U.S. risk-free rates—the most critical macro variable—because transmission of U.S. Treasury yields, interest rates, and exchange rates into our domestic market remains weak: China’s government bonds, interest rates, and currency operate within a largely self-contained system. A-share semiconductor, computing-power, server, and domestic-AI sectors don’t just track the global AI cycle—they also respond to RMB liquidity conditions, industrial policy, domestic substitution, technological self-reliance, and domestic capex. Crucially, they hinge even more heavily on domestic demand. That means the same shock—e.g., rising U.S. Treasury yields—carries fundamentally different implications: for AMD, it signals higher discount rates, valuation compression, and rising AI financing costs; for domestic Chinese computing-power firms, however, it may instead trigger countervailing forces—accelerated substitution, stronger policy support, and growing domestic order volumes.

Major AI tech giants’ earnings ‘bombed,’ wiping out nearly $1 trillion in market value: Is the AI bubble bursting—or undergoing a painful yet healthy ‘deep squat’?

It’s not just China’s A-share tech stocks—global tech equities have collectively endured an epic ‘black storm’ recently. As tech titans Alphabet, Amazon, Meta, and Microsoft released their latest quarterly results in rapid succession, Wall Street chose realism over enthusiasm: rather than rewarding continued AI exuberance, it turned bearish. This triggered a near $90 billion collapse in global tech market capitalization over just several days. Company Quarterly/Recent Revenue Core Cloud Business Growth Rate Quarterly/Rolling Free Cash Flow Market Reaction Microsoft (FY27 Q1) $92.28B (+19%) Azure: +48% +$18.6B (positive) Post-earnings up ~3% Meta (FY26 Q3) $64.60B (+26%) Driven by ads & AI applications −$2.12B (negative) Post-earnings rose then fell ~7% Alphabet (FY26 Q3) $127.58B (+25%) Google Cloud: +103% −$9.1B (negative) Post-earnings down ~7%; losses widened the next day Amazon (FY26 Q3) $201.94B (+12%) AWS: +39.3% −$42.36B (rolling 12-month negative) Post-earnings down >7% A pivotal question now dominating headlines is: With capital expenditures (CapEx) doubling while free cash flow plummets—does this signal the long-awaited ‘dual blow’ of the Davis double-squeeze—and the bursting of the generative AI bubble ignited two years ago? As a practitioner who has long observed the intersection of technology and finance, I offer this preliminary conclusion: The current global tech selloff doesn’t signify the death of AI technology itself—but rather marks AI’s industry entering a brutal yet inevitable ‘cleansing phase.’ It’s shifting decisively—from Phase One’s ‘blind infrastructure build-out and conceptual frenzy’—into Phase Two’s ‘cash-flow reckoning and agent-driven paradigm shift.’ The way to see through this storm is simple: don’t get lost in the fog of clickbait headlines. Instead, examine the underlying financial logic and industrial context—and seek the truths buried in Big Tech’s earnings reports. I. The root cause of the ‘earnings bomb’: Why did Wall Street suddenly lose faith? For the past two years, any Big Tech firm mentioning ‘AI’ during its earnings call could reliably expect its stock to rise. But by FY26 Q3, Wall Street’s true colors emerged: no longer captivated by PowerPoint narratives, investors zeroed in on income statements and cash-flow statements—revealing their unvarnished, profit-driven nature. What triggered panic this time were three ‘fatal hard flaws’ exposed in these giants’ financial data. 1. Free cash flow hemorrhaging: The world’s most profitable machines are bleeding cash. Historically, U.S. Big Tech firms have been dubbed ‘cash cows’—a label that also lifted AI-related stocks globally, including A-share AI names, for over half a year.

More drops for AI and tech stocks overshadow gains for most of Wall Street

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US stock markets closed mixed as tech and AI stocks declined, while falling Brent crude prices supported airline shares; geopolitical tensions continue to drive energy market volatility.

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More drops for AI and tech stocks overshadow gains for most of Wall Street

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