Asian Markets Have Bet Too Much On AI And Investors Are Getting Nervous

The comparison keeps surfacing in strategy notes, analyst calls, and whispered conversations at trading desks: what’s happening in Asian markets right now looks disturbingly similar to the late 1990s dotcom bubble that eventually vaporized trillions in wealth.

The Nasdaq Composite fell roughly 78% from its March 2000 peak to the October 2002 trough, wiping out fortunes and ending careers, yet a quarter-century later Asian markets have concentrated so heavily in AI-related stocks that they’ve created the same dangerous overreliance, leaving them vulnerable to a correction that could match or even exceed what dotcom survivors still remember with trauma.

The core question haunting anyone paying attention is whether investors have learned anything from history or whether they’re simply repeating the same mistakes with different acronyms and slightly better graphics.

The Concentration Crisis in Asian Tech Markets

The current AI rally reveals an eye-watering flood of cash into a remarkably narrow set of companies. Nvidia, Amazon, and Apple have become household names for retail investors, while Asian titans Samsung, SK hynix, Alibaba, and TSMC absorbed similarly massive inflows as the designated picks-and-shovels plays on artificial intelligence infrastructure.

Reuters market wraps chronicling Asia’s daily moves keep returning to the same handful of names, revealing just how concentrated the bets have become rather than capital spreading across diversified portfolios the way risk management textbooks suggest.

Asia’s dual role in the AI ecosystem explains why the region became ground zero for this concentration dynamic. Asian manufacturers function as the engine room of the global tech supply chain, with TSMC fabricating the most advanced chips, Samsung and SK hynix producing the memory that AI models devour, and Chinese firms assembling the physical infrastructure that houses it all.

Simultaneously, Asia represents one of the world’s most active adopters of digital infrastructure, from 5G networks to data centers, creating a feedback loop where the region both builds and consumes the technology driving the boom.

The semiconductor dependency has reached levels that should make any portfolio manager nervous. South Korea’s KOSPI sits heavily weighted to Samsung Electronics and SK hynix, meaning the index essentially functions as a leveraged bet on memory chip demand.

Japan’s exposure runs through chip-adjacent names and equipment makers, while Taiwan’s TAIEX is so dominated by TSMC that the company’s fortunes effectively determine whether Taiwan’s market rises or falls on any given day. This isn’t diversification but rather multiple ways to express the same concentrated bet.

SoftBank’s vulnerability crystallized when the stock suffered a roughly 14.3% single-day drop on a tech unwind headline, revealing systemic risk lurking beneath the surface. As one of the world’s biggest tech investors with tentacles reaching across continents and sectors, SoftBank functions as a barometer for how quickly concentrated positions can unravel when sentiment shifts.

That kind of intraday violence doesn’t happen to diversified portfolios but represents exactly what occurs when leverage meets concentration during moments of stress.

Market breadth problems have become impossible to ignore for anyone watching order flow rather than just price action. Chris Weston at Pepperstone has been quoted noting poor breadth and rotation out of leaders during Asian selloffs, meaning rallies are driven by fewer stocks carrying more weight while the broader market struggles.

This narrowing participation typically signals late-cycle dynamics where momentum chases a shrinking pool of winners, creating fragility that manifests suddenly once the music stops.

Chipmakers sell to hyperscalers like Amazon, Microsoft, and Google. Those hyperscalers buy capacity from each other and invest in each other’s ecosystems. Software firms building AI applications depend on the same computational stack, creating circular dependencies where everyone’s success relies on the same narrow infrastructure layer continuing to expand.

TSMC earnings presentations and Nvidia investor materials reveal this loop explicitly, with revenue growth tied to a small number of massive customers who themselves depend on continued AI investment to justify their own valuations.

Regional vulnerability comparison shows Asian markets consistently drop faster than Wall Street when AI and semiconductor sentiment turns negative. Korea and Taiwan indices can lose 3% to 4% on days when U.S. markets drift down just 1%, not because Asian companies are fundamentally weaker but because index composition creates mechanical selling pressure.

When Samsung and SK hynix fall, they drag the KOSPI with them in ways that diversified indices like the S&P 500 can absorb more gracefully across hundreds of constituents.

Valuation Reality Versus AI Promise

The earnings paradox has created cognitive dissonance that’s becoming harder to ignore. Samsung, SK hynix, and TSMC have posted strong quarterly prints that beat analyst expectations, yet traders increasingly question whether ever-higher prices can be justified even when the underlying businesses perform well.

Reuters coverage of SK hynix’s record earnings has been accompanied by commentary noting valuation looks stretched, revealing the tension between operational success and market pricing that often precedes corrections.

Record high territory made the subsequent volatility all the more jarring. The Nikkei 225 hit new all-time highs in 2024 and 2025, while the KOSPI reached multi-year peaks on chip optimism, creating the setup where maximum bullishness coincided with maximum risk. Seoul and Tokyo indices hitting records just before selloffs is the classic pattern where the last buyers in represent the fuel for the reversal, as those positions quickly flip from modest profits to painful losses.

The valuation challenge becomes stark when you examine price-to-earnings ratios and enterprise value-to-sales multiples for AI leaders across Asia. What looks strong in earnings growth appears terrifying when translated into the multiples investors are paying for that growth.

Financial Times Lex columns and Bloomberg Markets coverage have flagged these stretched valuations repeatedly, noting that even if earnings projections prove accurate, current prices embed optimism that leaves no room for disappointment.

At the same time, AI capital expenditure has exploded in ways that carry both promise and peril. U.S. Bureau of Economic Analysis data shows business investment carrying more of GDP growth contribution in certain quarters of 2025, with AI-related capex representing a substantial portion of that spending.

This echoes the late 1990s when telecom and internet infrastructure investment temporarily boosted economic statistics while creating overcapacity that would later devastate returns.

Bloomberg and Wall Street Journal analyses chronicling this capex surge note the risk that spending today doesn’t translate into returns tomorrow on the timeline markets have priced in.

Profit versus promise represents the fundamental tension that’s starting to crack. Markets are shifting from belief to verification, demanding measurable productivity gains rather than just narratives about transformation. Two years after ChatGPT captured global imagination and unleashed the current AI investment wave, pieces in the Journal and Financial Times keep asking the uncomfortable question: where are the profits?

Revenue growth is real, but converting that growth into sustainable margins that justify current valuations remains unproven for many names trading at premium multiples.

Moreover, Ray Dalio’s bubble indicator, which the billionaire investor occasionally updates and shares, has shown relatively high readings that align with elevated valuations across risk assets. While Dalio’s framework doesn’t predict timing, it identifies conditions where speculation has displaced fundamentals in ways that historically preceded painful corrections.

For allocators watching multiple indicators flash warning signals simultaneously, Dalio’s metric represents one more data point suggesting caution rather than adding to concentrated positions.

The Dotcom Playbook Asian Markets Are Following Again

History isn’t repeating exactly but it’s rhyming in ways that should make anyone who lived through 2000 to 2002 deeply uncomfortable. Current AI concentration mirrors late 1990s internet stock mania with eerie precision, from the handful of dominant names controlling index performance to the “this time is different” rhetoric that dismisses comparisons to previous bubbles. Financial Times Big Read pieces and Economist briefings have drawn explicit parallels between today’s AI enthusiasm and yesterday’s dotcom fever, noting similar patterns in valuation, positioning, and the way skeptics get dismissed as simply not understanding the transformation underway.

Similar characteristics keep surfacing when you compare late 1990s internet stocks with today’s AI plays. Unprofitable or low-profit companies command massive valuations based on future promise rather than current cash generation. Revenue multiples that would seem absurd in traditional industries get justified through growth rates and total addressable market estimates that may or may not materialize.

The same analysts who twenty-five years ago claimed “eyeballs” and “mindshare” mattered more than earnings now argue that AI infrastructure investment will inevitably generate returns despite uncertain timing and business models still being proven.

The “this time is different” mentality represents perhaps the most dangerous psychological trap because it contains a kernel of truth wrapped in catastrophic overconfidence. Every bubble generation believes their boom is fundamentally different from previous manias, and they’re always partially right because technology has advanced and adoption curves have accelerated.

Yet the fundamental dynamics of speculation, leverage, and crowd behavior remain constant across centuries.

The infrastructure investment trap represents perhaps the closest parallel between eras. Billions are being poured into AI infrastructure including chips, data centers, and power capacity, echoing how billions went into fiber optic cables, server farms, and telecom equipment during the dotcom boom.

International Energy Agency reports on data center electricity demand and Goldman Sachs research on AI capex cycles show spending trajectories that assume sustained growth, yet returns on that infrastructure depend on end-user demand materializing on the timeline and scale that’s been forecast.

The dotcom era proved that building infrastructure ahead of demand creates value destruction rather than value creation when the growth story disappoints, and there’s little evidence suggesting current AI infrastructure investment will avoid similar overcapacity risks if adoption curves flatten or business models fail to generate expected returns.