On Monday, the Dow Jones Industrial Average fell 1.7%, and several technology-linked names dropped far more sharply. Software and platform stocks that had ridden the AI wave for the past two years suddenly found themselves on the wrong side of sentiment; the selloff was not disorderly, but it was decisive. Billions were wiped off market capitalisations in a matter of hours.

By Tuesday, the shock had travelled 12 time zones east.

In Mumbai, the Nifty IT index plunged 21% for the month, marking its worst performance since the global financial crisis; what had begun as anxiety in American SaaS counters metastasised into a full-blown repricing of India’s technology sector. Domestic institutional investors – particularly LIC and mutual funds – saw nearly ₹1.18 lakh crore in notional value erode in weeks.

This was not a banking crisis; there were no collapsing lenders. It was not oil, not geopolitics, not a surprise rate hike.

It was scroll velocity.

Across continents, investors reacted not to a failed earnings season or a regulatory crackdown, but to the possibility that artificial intelligence may be more disruptive, more deflationary, and more labour-displacing than previously assumed; the speed at which that possibility travelled  – amplified through digital platforms – proved powerful enough to move markets in two of the world’s most systemically important technology ecosystems.

The crash did not start in one country; it unfolded across them, almost in sequence.

What Happened in the West

The ignition point was not a data release or a policy shift; it was a blog post.

Citrini Research, a widely read financial Substack, published a hypothetical scenario outlining how rapid advances in AI could trigger structural white-collar unemployment, wage deflation, and eventually a cascading stock market correction.

The scenario was explicitly framed as speculative; but its internal logic – that AI might hollow out service-sector demand faster than it generates new employment – struck a nerve.

The post described a feedback loop: AI improves, firms need fewer workers, layoffs rise, displaced workers spend less, corporate margins compress, and firms double down on AI to protect profitability – further accelerating displacement; the phrase “human intelligence displacement spiral” began circulating widely across social media feeds.

Markets responded quickly.

Software-as-a-service companies such as Monday.com saw sharp declines; platform-driven consumer names like DoorDash were hit on fears that AI-driven “agentic commerce” could weaken traditional moats such as customer loyalty and inertia. The Dow’s 1.7% fall may not sound catastrophic, but it reflected a broader de-risking across technology-heavy portfolios.

The speed of the move revealed something deeper than simple volatility.

Investors were not reacting to earnings misses or guidance cuts; they were reacting to possibility – to a plausible reconfiguration of how work, wages, and corporate margins might evolve in an AI-saturated economy.

For a market trading near historical highs and priced for sustained productivity gains, growth assumptions are everything; the AI rally of the past two years has rested on a delicate equilibrium – rapid productivity enhancement without catastrophic labour dislocation. The Citrini scenario challenged that equilibrium.

The so-called “SaaSpocalypse” framing captured the mood: if enterprises can replicate subscription software functions internally using generative AI tools, pricing power evaporates; if pricing power evaporates, margin compression follows; if margins compress in a sector trading at premium multiples, valuation resets can be swift.

In this case, a hypothetical future scenario moved real capital.

It exposed how sensitive modern markets are to forward-looking growth assumptions – particularly when those assumptions rest on technologies that are still evolving in real time.

This was the spark.

What Happened in India: Balance-Sheet Consequences

If the U.S. reaction was a valuation shock, India’s was more existential.

The 21% monthly collapse in the Nifty IT marks one of its sharpest declines since the global financial crisis; but the headline index number only captures the surface of the damage. Beneath it lies concentrated institutional exposure – and by extension, household savings.

Two of the country’s largest custodians of capital – Life Insurance Corporation of India and domestic mutual funds – sit at the epicentre of this repricing.

Data compiled from AMFI shows that mutual funds held ₹3,55,600 crore in leading IT stocks at the end of January; by February 24, that figure had declined to ₹2,80,933 crore – a notional erosion of ₹74,666 crore in less than four weeks.

LIC’s exposure tells a similar story; based on December-quarter shareholding disclosures, the market value of its holdings across the top 10 IT companies fell from ₹2,11,257 crore to ₹1,67,939 crore – a decline of ₹43,318 crore.

Together, these two institutions have seen roughly ₹1.18 lakh crore in value evaporate in a matter of weeks.

These are mark-to-market losses; portfolio adjustments may have occurred during the slide, and updated disclosures will clarify positioning. What is already clear, however, is the degree of concentration.

As of the December quarter, LIC held an 11.3% stake in Infosys and 5.3% in TCS; it also owned 11.5% of Tech Mahindra and 9.6% of LTIMindtree – precisely the counters that have borne the brunt of the selloff. Infosys is down 23% this month, TCS 18%, while HCL Technologies, Persistent Systems, and Coforge have shed 20% or more.

This is not peripheral exposure; it is structural.

Unlike 2008, when contagion was financial and global, today’s stress is thematic and technological; the proximate trigger is mounting concern that generative AI could erode the labour-arbitrage model that underpinned India’s IT ascent for three decades.

Managed services – often accounting for 22–45% of revenues at major firms – depend on large, people-intensive delivery structures; if AI tools automate testing, maintenance, and routine coding, demand for headcount-heavy outsourcing compresses.

Some market veterans argue the slowdown predates AI headlines; AI, in this view, is not the origin of weakness but an accelerant – intensifying pressures from commoditisation, pricing compression, and uneven Western demand.

Generative AI’s arrival changes the slope of risk; if clients can accomplish more with fewer engineers, the economics of scale shift and margin assumptions must be re-examined.

Even valuation cushions offer limited comfort; high dividend yields and historically strong free cash flows are backward-looking indicators. If revenue growth weakens further, payout sustainability becomes uncertain.

Brokerage recalibrations have followed swiftly; price targets have been cut, earnings trimmed, and frontline names downgraded. The thesis is not that Indian IT disappears, but that its revenue mix evolves – managed services may deflate while advisory and implementation work expands, introducing greater cyclicality and execution complexity.

What is indisputable is this: the India selloff is not merely a reaction to sentiment; it is a repricing of earnings trajectories embedded within the portfolios of institutions that safeguard millions of policyholders, SIP investors, and pension savers.

In the United States, AI fear compressed valuations; in India, it is interrogating the foundation of a growth model.

What began as a speculative scenario in Western markets translated within 24 hours into tangible balance-sheet consequences in India; the simultaneity was striking, the vulnerability shared.

And underlying both reactions is the same unresolved question: Is AI about to enhance productivity – or rewrite the economics of white-collar work faster than markets are prepared to absorb?

The Amplifier: When Smartphones Become Trading Floors

If the West provided the spark and India felt the fire, social media acted as the accelerant. The modern market no longer waits for brokerage notes or quarterly calls. It reacts to feeds.

The Citrini post did not remain confined to a niche financial readership. Screenshots circulated on X. Influencers condensed complex macro arguments into punchy threads. Chat groups debated whether SaaS business models were already obsolete. Within hours, a hypothetical scenario had mutated into a tradable thesis.

This is not entirely new – markets have always reacted to stories. What is new is velocity.

Retail participation in equity markets has surged dramatically in recent years. In the U.S., individual investors account for a record share of daily trading volumes. In India, systematic investment plans (SIPs) continue to channel monthly inflows into equities at unprecedented scale. The investor base is younger, digitally native, and increasingly dependent on real-time information streams rather than traditional research pipelines.

When millions of investors are connected through algorithm-driven platforms designed to reward urgency, outrage, and novelty, fear scales quickly.

Paul Donovan, chief economist at UBS Global Wealth Management, recently noted that economic perceptions are increasingly shaped not by data releases but by “sensationalized media output” consumed on smartphones. That gap between empirical indicators and perceived reality is becoming a macro variable in its own right.

Even seasoned analysts have expressed concern. Mark Zandi of Moody’s Analytics warned that markets appear increasingly driven by speculation rather than fundamentals. Torsten Slok of Apollo Global Management flagged rising “tail risks” linked to AI adoption uncertainty. And economists at Goldman Sachs cautioned that a sustained 10% equity correction could meaningfully dent U.S. GDP growth.

The concern is not merely about volatility. It is about feedback loops.

Markets fall. Investors see falling markets on their screens. They infer structural weakness. They reduce risk further. Asset prices decline again. Confidence deteriorates. Businesses respond by tightening spending. The narrative begins to shape reality.

In such an environment, the distinction between rumor and research blurs quickly. A scenario can behave like data. A thought experiment can resemble guidance.

The architecture of modern markets allows fear to travel faster than fundamentals can correct it.

Is AI Risk Being Mispriced?

The intensity of the reaction raises a deeper question: are markets genuinely repricing structural risk, or overshooting into panic?

The bullish case for AI remains intact in many respects. Proponents argue that generative AI will be embedded within enterprise software rather than replace it outright. Productivity gains could lower costs, expand margins, and stimulate new categories of demand. In this framing, disruption is transitional rather than destructive.

Economists like Tyler Cowen have argued that doomsday scenarios ignore basic macroeconomic identities. If AI dramatically increases output, income must be generated somewhere within the system. Aggregate demand does not simply evaporate. Prices adjust. Capital reallocates. Economies evolve.

Similarly, Robert Armstrong of the Financial Times questioned whether the “ghost GDP” perception holds up under scrutiny. If AI firms generate massive revenues, someone must be purchasing the goods and services being produced. Complete consumption collapse coexisting with record output stretches coherence.

And yet, uncertainty persists.

Artificial intelligence is not a single technology with predictable diffusion curves. It is a rapidly evolving ecosystem affecting coding, customer service, logistics, marketing, design, and analytics simultaneously. Unlike past industrial shifts, its impact is concentrated in white-collar services – sectors that constitute a large share of consumption in advanced economies.

That concentration explains the market’s sensitivity.

For U.S. equities, the issue is whether earnings growth embedded in premium valuations assumes a frictionless AI transition. For Indian IT, the issue is whether cost-arbitrage models can evolve quickly enough to remain indispensable in a world where automation eats into routine services revenue.

Markets are attempting to price a future productivity curve without historical precedent. That exercise is inherently unstable.

Fragile Markets in an Expensive Era

The broader backdrop makes the episode more revealing than alarming.

The S&P 500 remains not far from historic highs. Indian benchmarks, despite the IT rout, continue to trade at elevated multiples relative to long-term averages. Liquidity is abundant. Passive flows dominate. Retail participation is structurally embedded.

In such an environment, markets do not need catastrophic triggers to correct. They need justification.

When valuations are stretched and expectations are ambitious, sentiment becomes the weakest link. AI has been both the rally’s engine and its vulnerability. Optimism about productivity and margin expansion lifted technology stocks globally. The same theme, reframed negatively, can unwind positioning quickly.

This does not imply that the global economy is on the brink of collapse. It suggests something subtler: markets are navigating a technological transition whose economic contours are not yet measurable.

Uncertainty is high. Conviction is uneven. Information moves instantly.

And so a Substack post in New York can ripple through Mumbai’s institutional portfolios within a trading session.

The Last Bit, One Story, Two Markets, Shared Exposure

The events of the past two days reveal less about imminent recession and more about structural fragility in the AI era.

In the United States, the shock was valuation-driven – investors questioning whether growth assumptions tied to SaaS and platform businesses are sustainable if AI compresses pricing power and labour demand.

In India, the shock was model-driven – a reckoning with how automation may reshape the outsourcing engine that powered decades of export growth.

In both markets, the connective tissue was digital amplification. Fear scaled before balance sheets could respond.

The question now is not whether AI will transform the economy. That much seems inevitable. The real question is whether markets can remain stable while trying to price a transformation whose speed, distributional impact, and productivity payoff remain uncertain.

For now, one lesson is clear. The next global tremor may not begin with oil, banks, or geopolitics. It may begin with a post.