When Intelligence Becomes Dirt Cheap

Electricity made physical power cheap. The internet made information cheap. LLMs are making intelligence cheap. And we are not ready for what happens next.

This isn't a gradual shift. The cost of cognitive work is collapsing right now — not in some speculative future, but quarter by quarter, benchmark by benchmark. What took a junior analyst a week now takes an AI 30 seconds and costs less than a coffee. Multiply that across every knowledge worker on the planet and you start to see the scale of what's coming.

The Intelligence Price Crash

Every major economic revolution follows the same pattern: something expensive becomes cheap, and society restructures around the new reality.

• Electricity: Physical labor went from human/animal power to machines. Entire industries appeared. Others vanished.
• Internet: Information distribution went from expensive to free. Media, retail, communication — all transformed beyond recognition.
• LLMs: Cognitive work is going from expensive to nearly free. Analysis, writing, coding, reasoning, planning — all approaching marginal cost zero.

The pattern is clear, but people keep making the same mistake: assuming their industry, their job, their skill is somehow exempt. It wasn't true for factory workers in 1900. It wasn't true for travel agents in 2005. And it won't be true for knowledge workers in 2026.

White Collar Falls First

This isn't a prediction. It's already happening.

Klarna replaced 700 customer service agents with AI and reported better satisfaction scores. Duolingo laid off contractors because AI handles translation and content generation. Law firms are using AI to do document review that previously required teams of paralegals. Accounting firms are automating audit procedures. Marketing agencies are cutting copywriting teams.

The roles getting hit first share common traits: they involve processing information, following established patterns, and producing structured output. In other words — exactly what LLMs are good at.

• Analysts — AI can process data, find patterns, and write reports
• Junior developers — AI writes code, runs tests, fixes bugs
• Translators — already largely automated for most use cases
• Paralegals — document review, contract analysis, legal research
• Accountants — bookkeeping, tax prep, financial reporting
• Support agents — chatbots that actually work now

The uncomfortable truth: most white-collar work is pattern matching. And pattern matching is precisely what AI does better, faster, and cheaper than humans. The jobs that survive are the ones that require judgment in genuinely novel situations, deep relationship trust, or physical presence. That's a much smaller number than people think.

The Blue Collar Illusion

"Sure, AI can write emails, but it can't lay bricks."

This is the most dangerous form of complacency right now. Blue-collar workers see white-collar jobs getting automated and feel safe. After all, plumbing requires hands, construction requires physical presence, and logistics requires navigating the real world.

They're right — for now. But they're missing the acceleration spiral.

Here's the key insight: when you automate the engineers who build robots, robot development goes exponential. Right now, the bottleneck for humanoid robots isn't hardware — it's the intelligence to make them useful. LLMs are solving that bottleneck.

Look at what's already in motion:

• Figure — humanoid robots doing warehouse work, backed by OpenAI's models
• Tesla Optimus — general-purpose humanoid, iterating fast with Tesla's manufacturing scale
• Boston Dynamics — Atlas doing complex physical tasks, now paired with LLM reasoning
• 1X — EVE and NEO robots designed for homes and workplaces
• Agility Robotics — Digit already deployed in Amazon warehouses

The timeline isn't decades. It's years. White collar automation is ramping up now (2025-2028). Blue collar automation follows 1-2 years behind, once AI-designed robots reach production scale. By 2030-2032, very few jobs will be untouchable.

Education System Collapse

The current education model is built on a simple deal: invest 3-5 years learning a niche skill, then extract value from the job market for decades. The skill depreciates slowly enough that the investment pays off.

That deal is broken.

When entire professions get automated in 2-3 years, what happens to the student halfway through a 4-year accounting degree? Or the law student who takes on $200k in debt for a career that AI is already disrupting? They graduate into a job market that no longer needs their specific skills.

Student debt for skills that are worthless by graduation. That's not a dystopian fantasy — it's the math of automation speed vs education length.

What replaces the current model? Probably some combination of:

• Continuous learning: Short bursts of skill acquisition, not front-loaded degrees
• Micro-Credentials: Prove you can do X, not that you sat in a room for 4 years
• Learn-by-doing: Apprenticeship models where you learn alongside AI, not instead of it
• Meta-Skills: Learning how to learn, how to evaluate AI output, how to orchestrate agents

Universities that don't adapt will become the Blockbusters of education — charging premium prices for a product that's available better and cheaper elsewhere.

Three Scenarios

Nobody knows exactly how this plays out. But the range of outcomes is wide, and the path we take depends on choices being made right now — by governments, companies, and individuals.

My honest read? We'll get something between dystopian and transition, depending on the country. Scandinavian countries with strong social safety nets will manage. The US, with its worship of unregulated markets, will probably let a lot of people fall through the cracks before fixing anything. Developing nations could go either way — they could leapfrog with cheap AI, or get crushed by having nothing to export when labor is worthless.

The utopian scenario is possible but requires political will that doesn't currently exist. Post-scarcity is a technical possibility. Whether we distribute that abundance fairly is a political question — and history doesn't give great odds.

What To Do Now

This isn't a call to panic. It's a call to adapt — quickly and honestly.

• Be the human in the loop. Learn to steer AI, not compete with it. The person who can prompt, review, and direct AI output is infinitely more valuable than the person who does what AI can do, just slower.
• Own the stack, not the skill. Orchestrating multiple AI agents to solve complex problems > doing one task well. Think conductor, not violinist.
• Build capital, not just career. When labor is devalued, owning assets matters more than earning a salary. Equity, property, investments — anything that generates returns independent of your time.
• Relationships and trust. The one thing AI genuinely can't replicate. Your network, your reputation, the trust people place in you — that's your moat.
• Stay adaptable. No 5-year plans. Think in quarters. The world is changing fast enough that long-term career planning is a fiction. Be ready to pivot.
• Political engagement. UBI, AI taxation, education reform, labor protections — none of this happens by itself. The decisions being made right now will determine which scenario we land in. If you're not at the table, you're on the menu.

The intelligence revolution is here. Not coming — here. The cost of thinking has crashed, and everything built on the assumption that human cognition is scarce and valuable is about to be repriced. The question isn't whether society changes. It's whether we shape that change, or just get hit by it.

The future isn't something that happens to you. It's something you either build or get buried by.