SoftBank-led Asian tech selloff over AI infrastructure costs

When traders want to express a view on the entire AI trade in one ticker, they reach for SoftBank. Its roughly $65B exposure to OpenAI and an AI-heavy portfolio turn the stock into a high-beta proxy for the whole theme, which is why it dropped about 11% on June 26, 2026 while the broader Asian complex slid less ^[1]. The same dynamic played out in early November 2025, when SoftBank shed nearly $50B in market cap in a single week, its worst since March 2020, after OpenAI disclosed projected losses including a $74B operating loss in 2028 ^[6].

The leverage cuts both ways and explains the violence of the moves. Because SoftBank's value is so tightly coupled to a single private AI bet, any crack in confidence in OpenAI's path to profitability, including reports that its IPO could slip from 2026 to 2027, gets amplified into a double-digit equity drawdown ^[5]. That makes SoftBank less a company being repriced on its own fundamentals than a sentiment gauge: when the market's faith in AI returns wavers, SoftBank is where that doubt is registered first and most loudly.

The mechanism transmitting AI infrastructure costs into both chipmaker profits and consumer prices is what Bank of America's Vivek Arya calls the memory tax. Memory now accounts for roughly 35% of AI infrastructure capex, and Arya warns that 'elevated memory pricing could act as a tax on data center customer capex growth, while also leading to potential demand destruction in price-sensitive end markets like mobile and auto.' ^[2]As hyperscalers bid aggressively for high-bandwidth memory to fill data centers, the price of memory rises across the board, and that cost does not stay inside the data center.

The clearest evidence is on store shelves. Apple raised MacBook and iPad prices by as much as about $300 and Microsoft lifted Xbox prices by $100-150, both citing higher AI-driven component and memory costs ^[1]. That is the memory tax surfacing as a consumer price increase, effectively a third wave of inflation routed through AI hardware. The same surge that squeezes data-center budgets and end markets is, paradoxically, a windfall for the memory makers selling into it, which sets up the bifurcation that followed.

What made this episode different is that the market stopped treating AI as one undifferentiated trade. Investors began discriminating: HBM memory plays with locked-in multi-year contracts are seen as insulated, while rate-sensitive hyperscaler and infrastructure names carry the return-uncertainty risk ^[7]. The concentration that made the rout so violent in Korea, where SK Hynix and Samsung make up about half of KOSPI value and their slides tripped a circuit breaker, is the same concentration that now invites selective positioning rather than blanket selling ^[8].

Micron's earnings on June 25 crystallized the split and reversed the global selloff. The company posted revenue of $41.5B against $35.9B consensus, DRAM up 67% quarter over quarter, core data-center revenue up 653% year over year, 84.9% gross margins, and signed 16 five-year contracts worth $22B in commitments ^[2]. Those numbers reframed the memory tax as a chipmaker profit engine with contractually guaranteed demand through 2030, easing fears that AI demand had peaked ^[2]. Community discussion echoed the nuance, with some observers noting the original trigger was South Korean memory news rather than a US-led contagion, and pointing to Micron's print as the tell that demand was intact.

The selloff exposed a genuine analyst split over whether this is a buying opportunity or the early innings of a bust. Wedbush's Dan Ives calls it a gut check moment, arguing the AI build is still early but adding that 'the market's patience is not unlimited. 2026 is the year AI spending must start showing returns.' ^[9]Quilter Cheviot's Mamta Valechha captures the mood shift on the other side, noting investors 'have suddenly gone from the fear that you cannot be last, to investors questioning every single angle in this AI race,' with Amazon's roughly $200B capex guide in the crosshairs ^[4]. Morningstar's Michael Field reduces it to a binary: 'Either a big pay off if these investments come good, or a huge waste of shareholder's cash if it goes wrong.' ^[4]

The bear case is sharper still. Michael Burry argues AI earnings are inflated by understated hardware depreciation and that end-user demand is laughably small, warning that conditions resemble the final months of the dot-com bubble and estimating up to $176B in overstated profits across major firms from 2026-2028 ^[3][5]. The pressure point everyone agrees on is the capex-to-revenue gap: hyperscalers are planning roughly $600-700B in 2026 AI capex, a 36-71% jump, and more than $1.35T in Big Tech market value evaporated in a single week as investors doubted that spending would convert to commensurate returns ^[4]. The verdict now hinges on whether 2026 produces the returns Ives says the market is no longer willing to wait for.