Don’t Be Deceived by “Mindset Determinism”: The Rules of Survival in the AI Era Are Actually Hidden in Your “Geographical Location”
Recently, an article by American blogger Dan Koe went viral on the internet. He proposed a seemingly profound but somewhat motivational viewpoint: Society in the future will be divided into three types of people – resisters (who cling to old ideas), watchers (who bet that changes will pass), and curious learners (who actively embrace new tools). He believes that as long as you have the right mindset and become a “curious learner,” you can safely navigate the waves of AI.
The article became so popular because it offers a cheap form of comfort: it simplifies a complex structural crisis into a personal personality test. As long as you think of yourself as a “curious learner,” you feel safe.
However, as a scholar who observes economic trends, I must泼 some cold water on this: Dan Koe is half-right, but he misses the most critical variables – time and, even more so, location.
If you only consider mindset, you might think that learning more about AI will give you a chance to turn the situation around. But the reality is that where you are physically located and who you interact with every day determine whether you can truly overcome the challenges. Let me break down this in four points to help you see the true logic behind this transformation.
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1. History Does Not Repeat Itself Simply: Copyists Didn’t Become Editors; It Was Their Grandchildren Who Did
Dan Koe’s core argument is that with each technological revolution, skills have “abstracted upwards.” For example, the invention of the printing press led to the disappearance of copyists and the emergence of editors; the advent of the loom led to the disappearance of hand weavers and the emergence of machine operators. He suggests that the same will happen this time, and that if you are willing to learn, you can go from being replaced to being in control.
This logic has a huge flaw: it ignores the time cost of generational transition.
Let’s go back to the 15th century. After Gutenberg invented the printing press, Vespasiano da Bisticci, the largest manuscript supplier in Florence, did not transform into an editor. He closed his workshop in 1478, retired, and wrote memoirs, even criticizing printed books as coarse. Why? It took 50 years for the old craft to completely disappear. That 50 years was enough for two generations to make the career transition.
The key point is this:
- The copyists themselves did not become editors.
- The profession of editor emerged decades later, developed by a new generation (perhaps the grandchildren or apprentices of the copyists) when the printing industry had matured.
- The hand weavers also did not become machine operators. Factory owners preferred to hire women and children to keep wages low; the older weavers, with their higher wages and old habits, were bypassed. Most of them spent the rest of their lives in poverty, while their children went to work in the factories.
So, while the rule of “skills abstracting upwards” holds true, it is based on the premise that you have enough time for the next generation to adapt, not that you have to make the impossible transition yourself in one generation.
Dan Koe himself acknowledges that the time scale of this AI revolution has been compressed. In the past, the gap was linear; you could catch up in two years. Now it’s exponential, and by 2027, entry-level positions might no longer exist. To say there’s not enough time and then use a historical rule that requires time to prove its point is logically contradictory.
In plain terms: Don’t expect to transform slowly like the ancients. There’s no “buffer period” this time; it’s a “survival of the fittest” situation.
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2. With Exponential Gaps, “Personal Effort” Is the Least Important Variable
Dan Koe’s advice is to stay curious, experiment, cultivate taste, and write publicly. Sounds inspiring, right?
But if the gap is truly exponential, individual willpower is the least significant factor.
Exponential growth relies not on individual effort but on a compound interest structure. In such a structure, the starting point’s location matters far more than the rate of growth.
Imagine:
- Student A: Spends half an hour after work learning AI, is curious, and works very hard.
- Student B: Works at a top AI company, surrounded by experts who guide them through complex tasks and answer their questions.
After a year, the abilities of A and B are not on the same level. B might not even realize they are working hard; they are just doing their job.
What determines which side of the gap you’re on is not your personality but your “rate of exposure.”
What is the rate of exposure? It’s how many high-quality AI applications you come into contact with, how many knowledgeable people you meet, and how many real mistakes you see.
- In a place with only 2% AI professionals, your curiosity is a one-man show. No one corrects you when you make mistakes, and you’re isolated from useful information.
- In a place with 20% AI professionals, your curiosity is amplified by the environment. You see how others avoid pitfalls, optimize processes, and turn tools into productivity.
Dan Koe’s implicit assumption that location doesn’t matter and that the internet levels out geographical differences may have been true in 2015, but in 2026, it’s completely wrong. The real barriers to knowledge are not in the cloud but in the physical space around you.
In plain terms: Don’t just learn AI by looking at a screen. If there are no knowledgeable people or real business scenarios around you, your curiosity is hard to turn into productivity. The environment determines success more than your mindset.
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3. Real Fields Are Distributed by “Neighborhoods”: AI Density Determines a City’s Fate
Let’s look at some real data. Last week, CBRE released a ranking of tech talents in North America, and Forbes listed the top 50 AI companies in China. The data reveals a harsh truth: AI resources are highly concentrated.
In the United States:
- 37% of AI jobs are concentrated in four metropolitan areas: the San Francisco Bay Area, New York, Seattle, and Washington, DC.
- In San Francisco, 58% of office rentals in the first half of 2026 were by AI companies.
- The proportion of AI-related job listings in the Bay Area has increased from 20% to 57%.
In China:
- Beijing, Shanghai, Hangzhou, and Shenzhen account for 70% of the top 50 AI companies.
- Beijing has topped the list for three years in a row, with 15 companies this year, nearly twice as many as Shanghai in second place.
- Hangzhou’s rise is the most dramatic: it went from 2 to 7 companies, overtaking Shenzhen in third place.
- In the field of embodied intelligence, there are 2,439 companies in Shenzhen, 1,833 in Beijing, 1,620 in Shanghai, and 1,105 in Hangzhou.
When you overlay this data with Dan Koe’s classification, you see:
1. **In places like San Francisco or Shenzhen, it’s difficult to be a “resister.” Your landlord, colleagues, and people in the coffee shop below your office are all pushing you to change. Your resistance is powerless because the entire ecosystem is moving forward.
2. **In a city with almost no AI professionals, it’s also hard to be a “curious learner.” You have no peers, no one to ask questions of, and no one to avoid mistakes that could save you time. Your curiosity is a lonely effort.
So, real fields are not divided by personality but by location. If you want to be a pioneer in the AI era, move to a city that is AI-friendly and has a high concentration of AI companies rather than staying in a city where people are waiting or resisting.
In plain terms: Don’t pretend you understand AI just by looking at a screen. How many AI companies are in your city? How many AI-related jobs? If the answer is “almost none,” no amount of effort will help you succeed. Choosing a city is choosing your AI environment.
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4. In the Next Five Years: “Species Differentiation” Between Cities Has Already Begun
Following this logic, three things will likely happen over the next five years:
1. “AI density” and the number of innovative companies will become new indicators of city competitiveness. Instead of comparing GDP, we will compare the number of AI engineers per square kilometer.
2. Office rental data will reveal the truth earlier than official statistics. Government data lags, but companies don’t lie about their office rentals. Places with heavy AI activity are the centers of innovation.
3. Cities of average quality will face a tough selection process. The number of top cities is increasing (from 9 to 16), indicating that niche markets are emerging, but the leading cities will capture 70% of the opportunities. This means cities must either find a unique niche (like Hangzhou’s embodied intelligence) or become the “17th” in a well-established field.
Being the “17th” means having no ecosystem. Without an ecosystem, there’s no talent flow, no knowledge exchange, and no compound growth.
Dan Koe says that those who understand this within the next 12 to 36 months will be like a different “species.” This is probably true, and there’s a biological principle behind this: geographical isolation.
In plain terms: The gap between people in the future will not just be in skills but in ecosystems. Being in a city with a strong AI atmosphere means you’re “evolving”; being in an AI-desert means you’re “degrading.” Don’t let your geographical location limit your AI potential.
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Conclusion: Stop Doing Personality Tests; Make Geographical Choices
Dan Koe’s article is popular because it gives the illusion of safety through a positive mindset. But reality is harsh: The AI revolution is not a personal journey but a collective migration.
- Resisters will be left behind as their environment accelerates away from them.
- **Watchers will be marginalized because their environment lacks the opportunities for growth.
- Only curious learners in high-density AI environments can truly become masters of the technology.
So, if you want to survive and thrive in this transformation, the first step is not to enroll in an AI course but to ask yourself: Does my current location support my curiosity?
If the answer is no, moving or remotely integrating into an AI-friendly ecosystem is more important than any mental adjustment.
Remember: In an era of exponential change, location is destiny.