From Hoarding Answers to Hunting Context: How Education Must Change in the AI Age.
For centuries we trained people like machines: memorize, regurgitate, pass the test. That era is over. In the age of AI, speed and recall are commoditized.
Before the printing press, scarcity defined knowledge: gatekeepers, slow spread, and a zero-sum game of access. The internet flipped that script. We live in abundance now—information at our fingertips—but abundance without navigation becomes noise. The indefinite optimists celebrate unfettered access, but too much data without context breeds complacency, echo chambers, and mediocrity, not breakthroughs.
For centuries we trained people like machines: memorize, regurgitate, pass the test. That era is over. In the age of AI, speed and recall are commoditized. Machines are the ultimate specialists: they ingest oceans of data, process it in seconds, and return precise answers. What matters now isn’t having facts—anyone can summon them—but asking the right questions and creating the context that makes answers meaningful. This is not incremental; it’s a seismic shift toward education that builds creators, not copycats.
Two equations cut through the noise:
Data = Information × (Noise)^2. Today’s torrent of data is amplified by noise—misinformation, bias, and irrelevant friction—that squares itself and drowns signal in confusion.
Value = Information × (Context)^2. Raw information without context is like a puzzle piece without the box. Context multiplies meaning; square it, and insight becomes transformative.
How do we act in this landscape? Think like a detective: first, “cure” the data—strip away noise to reveal verifiable facts. Second, hunt for context—the rules, assumptions, incentives, and history that let you put facts into a working framework. The first task is hygiene; the second is creative, strategic, and uniquely human. We should not compete with AI on speed; we should outthink it by probing the unknowns it can’t intuit.
Richard Feynman gets to the point. He demolished superficial memorization and insisted on understanding principles. His method—explain something as if teaching a child, find the gaps, simplify—forces you to reconstruct knowledge from first principles. “What I cannot create, I do not understand,” he said. In an era where AI owns the answers, Feynman’s technique directs us to chase questions that reveal context and causal structure—the very things that square the value equation.
Isaac Asimov saw parts of this coming. He predicted personalized, self-directed learning through computers long before AI tutors were possible. More importantly, he warned that “science gathers knowledge faster than society gathers wisdom.” We need systems that filter knowledge into wisdom; we need learners who know how to separate signal from noise and convert information into practical, creative work.
Reframing risk: from threat to information
Risk taking is fundamental for real education. The whole concept of risk is completely wrong in Western societies. Risk is associated with failure, loss, volatility, and accidents. However, risk is not that. Risk is a challenge—something you don't know, where there is new information in the system and an opportunity to understand a new context. If we build a team able to understand this new context, we can monetize this challenge; if not, we lose. However, we still gain some kind of information, and if we try enough times, we will end up monetizing it. If we do not have challenges, there is no room for learning.That is the reason I think investing is fundamental in any educational program.
Thanks for reading,
Guillermo Valencia A
Co Founder MacroWise
Medellín, Colombia