Hello! I'm your economic analyst and financial news partner. This article from Havenlon Labs is a profound call for action in the field of cognitive economics. It doesn't dwell on the superficial concern of whether AI will take away people's jobs; instead, it exposes a fundamental flaw in the modern knowledge economy: we have always used humans as the sole measure of intelligence and value, and AI is changing this foundation.
To make it easier for you to understand, I'll summarize the main points in one sentence and then break them down into five dimensions, explaining them in plain language.
📌 Summary of Key Points
In one sentence:
The emergence of AI doesn't make humans less intelligent; rather, it removes humans' monopoly on the measurement of intelligence. What we used to consider difficult was actually due to the complexity of the problems, but now we realize that many of these challenges were simply because the human brain is slow, expensive, and unable to process information in parallel. AI is making cognitive abilities, which were once scarce, more accessible and affordable, forcing society to redefine what value is and to restructure systems designed around human time.
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🔍 In-depth Explanation: Five Dimensions in Plain Language
1. Stop saying “AI is too fast” – you're using human standards to measure machine speed
Plain Language:
We often complain that AI is developing too quickly. But “fast” is relative; fast compared to what? Humans. Just as cars made 30 km/h seem fast before they existed, now 100 km/h seems normal. The article points out that the speed at which humans can generate knowledge is limited by our physical constraints: we need to sleep, eat, go to school, and rest. A mathematician can only think intensively for a few hours a day and can't replicate themselves thousands of times to test different solutions. So when an AI system solves a problem that would take a human mathematician 10 years, we marvel at its speed. But this is a misunderstanding. AI isn't just a faster version of humans; it's a completely different type of cognitive machine.
Economic Perspective:
In economics, this is called the failure of the reference frame. Our previous benchmark was the human limit, but now that has changed. Using the old benchmark to evaluate new things is like using “horsepower” to measure the energy of a nuclear reactor—while it's possible to make the conversion, we lose sight of the essential differences.
2. What you think is “difficult” is actually “expensive for humans”
Plain Language:
We often find certain math problems or code logic too hard, believing only geniuses can solve them. But the article reveals a harsh truth: much of what seems difficult is actually due to high costs. Forcing humans to perform complex calculations by hand is a test of their brain's limits, while for computers, it's basic. Understanding a sarcastic statement might be intuitive for a three-year-old, but challenging for early AI. We tend to define what we're good at (like intuition and emotion) as “ordinary” and what we're not good at (like parallel computing) as “difficult.” Math seems sacred and difficult because training a top mathematician requires a huge investment in time, money, and opportunities. AI is separating the cost of acquiring abilities from the value of those abilities themselves. Just as a crane can easily lift ten tons of stone without feeling like a genius, AI makes cognitive abilities that used to require 20 years of training now available at low cost.
Economic Perspective:
This is an example of decreasing marginal costs: the cost of acquiring advanced cognitive abilities used to be high (long-term education), but now AI has reduced this cost to near zero. When the cost of producing intelligent outputs drops significantly, their market scarcity disappears.
3. Mathematicians' anxiety is justified: scarcity is disappearing, and their jobs are at risk
Plain Language:
Why are mathematicians like Steven Strogatz worried? Because the foundation of their profession—scarcity—is disappearing. The old logic was: high-quality mathematical knowledge was rare, so those who could produce it were valuable. Now, AI can generate thousands of proofs or ideas in a day, making this task cheap. So who decides which proofs are good or worth publishing? If producing proofs becomes as easy as printing documents, mathematicians' core value shifts from solving problems to filtering and judging. It's like painters who used to rely on their skill in drawing; now, if they only do that, they can't survive. They must adapt to roles in aesthetic judgment, artistic expression, or curating.
Economic Perspective:
This is a reversal of supply and demand. When supply (AI-generated results) increases infinitely while demand (human attention and recognition) remains limited, the value of the profession declines. To maintain their value, mathematicians must move up the value chain to roles involving decision-making, responsibility, and meaning-making.
4. Business reality: AI reduces the scarcity of certain abilities, not the abilities themselves
Plain Language:
Many bosses still wonder if AI can think like a human or if it has consciousness. But this question is irrelevant in business. What matters is whether AI can do what experts used to do in a shorter time. For example, if a professional photographer was the only one who could take good photos, now smartphones can do it just as well. AI doesn't replace the photographer's artistic skill; it changes the scarcity of the market for basic photography. The biggest impact of AI on professions is the reordering of value: in the past, being able to execute tasks was valuable; in the future, being able to make judgments, correct mistakes, take responsibility, and explain the meaning of results will be more valuable.
Economic Perspective:
This is a revaluation of factors of production: as the cost of intelligent production decreases, the value of judgment, choice, and responsibility increases. Companies and individuals need to shift their resources from buying computing power to buying the ability to make informed decisions and take responsibility.
5. The biggest crisis: Our systems are still based on the “human clock”
Plain Language:
Almost all of our societal rules are designed around human speed: company approvals take days or weeks, legal regulations years, education spans decades, and research reviews quarterly or annually. But AI operates at millisecond speeds, in parallel, and 24/7. This leads to a huge mismatch: AI can generate the equivalent of 10 years of research in an hour, while our regulatory bodies still meet for months to decide whether to regulate it, and universities still teach students knowledge that AI has already mastered. This isn't about AI being fast; it's about our outdated operating systems. Using rules from the horse-drawn carriage era to manage high-speed traffic leads to chaos and inefficiency. We need to redesign how we verify AI results, determine responsibility in case of errors, and evaluate its innovations.
Economic Perspective:
This is a surge in institutional friction costs: when technology evolves much faster than our systems, efficiency declines, and risks increase. The future competition won't just be about AI technology but also about the agility of our institutions. Those who can quickly restructure regulation, education, and evaluation systems will have a competitive advantage.
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💡 Tips for Everyone
1. Stop self-deprecation or blind worship: Don't think you're useless because you can't compete with AI, nor think of AI as a god. AI is a tool, another form of cognition.
2. Reframe your value: Ask yourself which parts of your job can be automated and which parts involve judgment, choice, responsibility, and meaning-making. Focus on the latter.
3. Focus on verification and connection: In an era of information overload, the ability to verify facts, integrate information, and build trust is more valuable than just acquiring it.
4. Understand the shift in standards: The world no longer revolves around humans alone. Learning to coexist with intelligent systems that operate at different speeds and with different logic is essential for future citizens.
To conclude, let's use the most beautiful sentence from the article:
“A civilization's true strength may lie in its ability to understand itself again after creating a cognitive system completely different from its own, rather than expecting the new world to operate according to old standards.”