When the world stops paying for your brain

Here’s the weird thing: for most of modern history, knowledge was expensive. Not just in cost, but in time, access, and skill. If you wanted to learn something, you had to pay for the book, get into the university, or sit through the dense lecture. The payoff was obvious — the more knowledge you had, the rarer you were. And rare things pay well.

Now we’ve gone from rare to ridiculous. Knowledge is everywhere, in every format, for free. And now AI has come along and said, “You know that intellectual work you used to do? Yeah, I can do it in 0.2 seconds.” What used to be a ticket into the upper tiers of the economy is quickly becoming table stakes — and sometimes, even free.

So the question isn’t if intellectual work will change — it’s what replaces it as the thing we actually value.

Why it matters

When knowledge is everywhere, the value isn’t in knowing — it’s in choosing what matters.

The four big phases of knowledge work

We can actually map this out like a timeline:

The four stages of knowledge work, from restricted to commodified

Phase 1: Restricted knowledge, restricted consumption, high reward.The pre-internet days. Knowledge was locked up in universities, professional networks, and specialized books. Only a small group could get it, fewer could use it, and they were paid well for it.

Phase 2: Available knowledge, restricted consumption, high reward.Google, Wikipedia, open-source journals arrive. In theory, anyone could access knowledge. But it was still dense. You still needed time, skill, and maybe some background just to make sense of it.

Phase 3: Available and digestible knowledge, widespread consumption, moderate reward.This is the TED Talk era. MOOCs, YouTube explainers, blog posts that summarize academic papers. Knowledge becomes snackable. People everywhere start consuming it. But with more people able to apply it, the economic value starts to drop.

Phase 4: AI-assisted knowledge and application, everywhere.This is now. AI can find the right info, summarize it, apply it, and even explain it in a style you like. The “doing something with knowledge” part, the thing that made it valuable, is no longer scarce.

So what’s left?

If knowledge and its application are everywhere, what’s scarce? A few things stand out:

Critical thinking matters

Having the answer isn’t rare. Knowing which answer works in this situation, for these people, right now, and that’s harder. Context is value.

Meta-work matters

Instead of just creating knowledge, there’s designing how it flows — systems, workflows, and human-machine collaboration. Orchestration, not authorship.

Pre-work matters

AI can process and think with you, but it cannot as of yet collect phenomena from the world as effectively as humans. Feeding the grand scheme of AI machines becomes pre-knowledge, and valuable work.

Applied work matters

As a PhD, I am sad to say that those beautiful "in the ether" musings on knowledge have niche value, but the applied, on-the-spot knowledge gains momentum. The electricity of knowledge cannot be store in batteries, it has value only in the circuitry it powers.

Trust matters

When AI can give you any fact, what people want from humans is trust, credibility, and a sense that you’ve been there. Knowledge is abundant; relationships are not. To be the source of credibility is not a commodified good, so there's value in niche, specialized verification, which is different from owning knowledge.

Key takeaways

• Knowledge used to be rare. Now it’s everywhere.

• The hard part isn’t finding info — it’s filtering it.

• Judgment, trust, and orchestration will matter more than raw knowledge.

• Humans will pivot from creating to curating and connecting.

  
  

Further reading

1. Davenport, T. H., & Kirby, J. (2016). Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. Harper Business.

2. McAfee, A., & Brynjolfsson, E. (2014). The Second Machine Age. Norton.

3. Galloway, S. (2023). The Algebra of Wealth. Portfolio.

4. Harari, Y. N. (2018). 21 Lessons for the 21st Century. Spiegel & Grau.

5. Morozov, E. (2013). To Save Everything, Click Here. PublicAffairs.

6. Andreessen, M. (2023). “The Techno-Optimist Manifesto.”

7. European Commission (2024). “The Future of Work: Skills and Innovation.” White Paper.

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SUMMARY PROTOCOL

TOPIC The changing value of intellectual work in the age of abundant AI-driven knowledge.

PROBLEM When knowledge and its application are automated, the old rewards for intellectual work collapse.

QUICK TAKEAWAY Scarcity has moved from knowledge itself to curation, judgment, and trust.

CORE CONTENT Four phases: from restricted knowledge/high reward → to abundant, AI-applied knowledge/low reward. The future is in context, filtering, and orchestration.

POLITICAL LANDSCAPE AI challenges the role of credentialed experts, disrupts education, and shifts economic power from knowledge producers to platform owners.

QUICK ACTION Agent should move from producing information to designing systems and trust-based experiences.

RISK OF DOING NOTHING Agent will be drowned out by machine-generated noise and lose competitive relevance.

FUTURE PROTOCOL Focus on building filtering systems, honing judgment skills, and establishing credibility as a human in the loop.

— Maintain signal. Filter noise. Humanize the process.

Formatted and co-written with an LLM.