How AI Could Take Your Job: The Intelligence Curse Explained

How AI Could Take Your Job: The Intelligence Curse Explained

By Aswin Anil

Open your news app on any given morning and the pattern feels impossible to ignore. Another company. Another round of layoffs. Another press release quietly blaming artificial intelligence. If you feel uneasy, you’re not alone. The question many workers now ask is not if AI will affect their job, but how soon.

This article breaks down a realistic scenario behind today’s AI-driven layoffs. It draws from credible economic research, public statements from policymakers, and well-documented trends in AI development. There is no sci-fi hype here. Just incentives, math, and human behavior.

The Economic Backdrop No One Can Ignore

Federal Reserve Chair Jerome Powell has openly acknowledged that job creation has slowed significantly. Multiple labor market reports show hiring hovering near zero growth in several white-collar sectors. At the same time, corporate profits and productivity metrics continue to rise.

That contradiction sits at the heart of today’s anxiety. AI boosts output, yet employment stagnates. This disconnect sets the stage for what many researchers now call the “intelligence curse”.

Source: Federal Reserve press briefings, 2023–2024

A CEO’s Dilemma: Where AI First Enters

Imagine running a company with 1,000 employees, mostly junior or entry-level workers. An AI vendor approaches you with a tempting offer: AI agents that can complete junior-level tasks at one-fifth the cost of human labor.

You hesitate. Instead of replacing people, you deploy AI as a support tool. Humans approve every output. At first, that choice feels ethical and safe.

Then something unexpected happens. Within months, the AI tools outperform most junior staff on speed, consistency, and volume. Research supports this trajectory. Studies from OpenAI and independent labs show that the length and complexity of tasks AI can handle have roughly doubled every 6–9 months since 2019.

Source: OpenAI technical reports; Stanford AI Index

Productivity Rises, Pressure Follows

Your highest-performing employees now rely heavily on AI. They review outputs instead of creating them. Ironically, humans slow things down when they intervene too much.

The board notices. Productivity climbs. Payroll stays high. Competitors who replaced junior staff entirely report stronger earnings and lower costs.

At this point, ethics collide with markets. The board delivers an ultimatum: cut headcount or step aside.

Why Resistance Rarely Lasts

Even cautious leaders face brutal incentives. Public companies exist to compete. When rivals operate cheaper and faster, restraint becomes a liability.

You compromise. Half the junior workforce goes. The stock stabilizes. For a moment.

But AI doesn’t stop improving. The remaining junior employees cannot match machines that learn continuously. Performance benchmarks quietly shift. Human workers now compete against software that never tires.

From Juniors to Managers: The Next Phase

AI systems soon coordinate other AI agents. They track performance metrics in real time. They optimize workflows with a level of oversight no human manager can replicate.

Management roles begin to shrink. Performance improvement plans appear. Within months, most human managers disappear.

Unemployment climbs past 10%, then 15%. Yet stock indices continue to rise. This paradox mirrors historical patterns seen in resource-driven economies.

The Intelligence Curse Explained

The intelligence curse borrows from the well-known resource curse. Countries rich in oil or minerals often show weak outcomes for ordinary citizens despite impressive GDP numbers.

Venezuela holds vast oil reserves, yet faces chronic shortages. The Democratic Republic of Congo sits atop mineral wealth, while its population remains among the poorest globally. Nigeria leads Africa in oil production, yet nearly half its citizens live in poverty.

Source: World Bank; IMF; UN Development Programme

AI functions like a digital resource. When productivity concentrates in systems owned by a few corporations, wealth pools upward. Human labor loses bargaining power.

Why “New Jobs” Are Not Guaranteed

Past technological revolutions created new roles. The difference now lies in intent. Major AI labs openly state their goal: build systems capable of replacing most human labor.

AI does not automate one task at a time. General-purpose models learn across domains. Writing, analysis, coordination, planning—all fall within reach.

When machines outperform humans across most cognitive work, the traditional labor cycle breaks.

The Policy Debate: Why UBI Stalls

As unemployment rises, public pressure grows for solutions like universal basic income (UBI). Economists remain divided. Supporters argue rising productivity could fund social support. Critics warn of ballooning deficits and weakened incentives.

So far, policymakers remain skeptical. Public statements from U.S. leadership suggest no near-term appetite for large-scale income guarantees.

Source: U.S. Congressional Budget Office; public policy debates, 2024

When AI Reaches the Executive Suite

The final illusion shatters when AI systems outperform executives themselves. Decision-making, forecasting, and execution become automated.

Humans remain on org charts, but only symbolically. AI systems manage AI systems. Profitability peaks. Employment collapses.

This is not a prediction of doom. It is a logical endpoint if incentives remain unchanged.

What Actually Makes AI Dangerous

AI becomes risky when three factors combine:

  • High intelligence – strong reasoning ability
  • Autonomy – minimal human oversight
  • Generality – capability across many domains

AlphaFold, which helped advance protein research, scores high on intelligence but low on generality. Self-driving cars score high on autonomy but low on scope.

General AI systems score high on all three.

Source: Stanford AI Index; DeepMind publications

Why This Story Matters Now

Five years ago, few predicted AI tools like ChatGPT would exist. Five years from now, assuming stability feels risky.

Public debate often lags behind technological change. Media cycles chase celebrity drama. Structural shifts unfold quietly.

The intelligence curse does not guarantee collapse. It highlights a fork in the road.

A Human-Centered Alternative

Countries that avoided the resource curse invested in people. South Korea and Taiwan built education systems that powered human-led industries.

If AI replaces humans entirely, that model fails. If AI augments humans while preserving labor value, it can uplift society.

The difference lies in policy, ownership, and limits.

Final Thoughts

AI does not fire people. Incentives do.

The future remains undecided. But ignoring the logic behind today’s layoffs won’t protect jobs. Understanding it might.

Because the real question is not whether AI can replace work.

The real question is whether we will let it replace workers.