
How It All Began (2025–2026)
What started as seemingly rational corporate cost-cutting became a destructive economic force:
AI tools rapidly improved, especially agentic systems capable of building and adapting software, performing research, legal work, advice, and much more.
By late 2025, enterprise IT teams began using AI agents to replicate functions previously outsourced to expensive SaaS providers. AI worked 24/7, did not require salaries or healthcare, and drastically lowered marginal labour costs.
This triggered an investment cycle where companies laid off humans and invested the savings into even more AI capability — a negative feedback loop with no built-in brake.

At first, economic headlines still looked strong: productivity soared, nominal GDP grew, and corporate profits hit record levels.
But a deeper problem developed — the economy lost real income for workers, especially white-collar professionals whose jobs vanished first.
The Intelligence Displacement Spiral
The core mechanism of the crisis was what is today known as the “human intelligence displacement spiral”: AI replaced human labour, especially high-paid white-collar work. Displaced workers earned less or became unemployed.
With lower income, consumer spending — especially on discretionary goods — collapsed. Weak consumption slowed demand for goods and services. Firms responded by squeezing costs further with more AI.
Unlike traditional innovation cycles — where displaced workers eventually find new jobs that humans can do — AI agents could now perform the very tasks humans would shift into, preventing a robust labour resettlement.
As a result:
Consumer spending fell sharply, undermining the engine that historically drove economies. Measured GDP remained deceptively high, because AI output showed up in national accounts even though machines spent nothing — a phenomenon dubbed “Ghost GDP.”
Traditional economic indicators became misleading. Production remained high, but money did not circulate through households.
This divergence — between high measured output and low real economic activity — undermined confidence, weakened markets, and destabilized the financial system.

Financial Contagion and Systemic Risk
In the mid-to-late 2020s, what began as sector-specific disruptions in software and services expanded into a full blown systemic risk:
Software and technology companies, once centers of innovation and stable earnings, saw cascading downgrades, defaults, and valuation collapses as recurring revenues crumbled.
Private credit markets, heavily exposed to tech and software debt, faced liquidity stress as assumptions about perpetual growth dissolved.
Legacy sectors that once seemed safe — payments, logistics, intermediation and financial services — were disrupted as AI removed human friction and extracts fees, undermining their economic moats.
Financial markets experienced sharp drawdowns, with broad indices down significantly from their 2026 peaks. Investors become unnerved not because AI failed as a technology, but because it succeeded too well in displacing labour without creating compensatory consumer demand.
International Ripple Effects
The crisis was not confined to the United States. According to analysis of the scenario, emerging economies with large services export sectors — like India — suffered uniquely. Countries whose growth models relied on low-cost human labour in services and IT became especially vulnerable as AI could produce equivalent work at near-zero marginal cost (limited only by electricity).
Major Indian IT firms saw contract cancellations accelerate, exports fall, and the national currency depreciate sharply.
The broader point was that global economic structures built around human capital got destabilized as AI systematically replaced it.
Core Takeaways
1. AI productivity gains did not automatically translate into broad economic prosperity. Productivity merely shifted wealth toward the owners of compute and capital; workers lost out as their labour lost value.
2. Consumption — not production per se — drove real economic growth.
Artificially high output numbers could not mask underlying weakness as households lacked income to spend.
3. Traditional economic models and policy tools failed when automation cut across the core consumer base.
Central banks and fiscal policymakers found themselves ill-equipped to manage this novel disruption.
Conclusion
The 2028 Global Intelligence Crisis reframed the AI debate: it challenged the assumption that greater automation always benefits society broadly. Instead, it created a future in which AI’s triumph in productivity collapsed the foundation of modern economies — the income and spending power of humans themselves — leading to lower real economic activity despite record output figures.
It became a powerful reminder that technological progress alone does not guarantee shared prosperity, and that policymakers and investors needed to think deeply about how gains from automation could be distributed across society.
In musing….. Shakti Ghosal
Acknowledgement : The 2028 Global Intelligence Crisis – http://www.citriniresearch.com/p/2028gic




























