A few months ago, some acquaintances of mine launched a line of vitamins. One evening, over tea, they showed me their Shopify site. The images were flawless: bottles bathed in the golden rays of a setting sun, shimmering splashes of Vitamin D, and those “perfect” angles that compel you to hit the “Buy” button. I asked how much the photoshoot cost. It turned out that a guy from a small village generated the entire thing in a few hours using an AI neural network.
Previously, a project like that would have cost upwards of a thousand dollars: a studio, an experienced photographer, lighting technicians, retouchers, and equipment rentals. An entire chain of people earned their living from every shoot like that. Now, that chain has simply ceased to exist. It wasn’t downsized; it vanished. And the people who worked in it will never return to the profession, because the profession no longer exists. This isn’t a scary fairy tale about robots; it is something that has already happened.
The Domino Effect No One Is Calculating
When people talk about automation, they usually cite a single figure—the number of people laid off. But the real scale is hidden within the chains of dependency.
Take a warehouse as an example. An inventor-professor I go to the sauna with recently launched a drone that flies all night between six-meter-high racks, reconciling inventory and immediately entering the data into the accounting system. No lighting, no forklifts, and no people required.
It’s clear how many warehouse clerks it replaced. But how many shuttle bus drivers stopped transporting those clerks to their shifts? How many cafes at the entrance lost their regular customers? How many garment factories lost orders for work uniforms? Automation doesn’t just kill one position; it pulls the rug out from under entire employment ecosystems.
The same happened to translators. Ten years ago, I was preparing a book for publication in the States and spent tens of thousands of dollars and months of work with top specialists. Today, out of a hundred translators, only one would find work—the one handling literary or certified translation. The market has simply discarded the other ninety-nine. Not because they did a poor job, but because a neural network does the same thing faster and for free.
According to estimates from major analytical centers, by 2030, restructuring or large-scale downsizing will affect one in five jobs on the planet. Goldman Sachs puts the figure at 300 million—that’s one in every ten workers on Earth. And in developed economies, where the share of formalizable labor is higher, this percentage will be even greater.
Accountants, call center operators, administrators, secretaries—these are professions with a high proportion of repetitive, easily algorithmized tasks. AI agents are already answering incoming calls, scheduling appointments, and handling correspondence. These are not pilot projects for major corporations—they are tools available to small businesses right now.
Why We Weren’t Prepared for This
When you see the scale of what is happening, a logical question arises: where were those who were supposed to foresee this?
Governments saw it, analysts made forecasts, and labor market statistics have been accumulating for decades. But the education system continues to operate according to 19th-century templates. Its goal was never to raise people capable of adapting to change. It produced literate, manageable, and predictable participants in the economy—those who take their place in the structure and don’t ask unnecessary questions.
A child is given a clear route: school, university, job. This route worked as long as the world changed over the course of decades. If you studied to be a lawyer, after five years of practice, you became a lawyer—adjusted for experience, but within the realm of expectations. Today, design as a profession has changed so much in the last two years that educational programs simply haven’t had time to notice. A student studying physical mock-ups and traditional techniques today enters a market where only those who know how to manage neural networks are competitive.
States try to slow this process down where they can. Self-driving cars have been technically ready for mass implementation for several years. But old regulations are intentionally standing in the way to avoid throwing millions of drivers, auto mechanics, and the entire surrounding infrastructure onto the streets. This isn’t bureaucratic stupidity; it’s a conscious deceleration for the sake of social stability.
The problem is that it’s impossible to slow down the replacement of photographers, translators, or accountants because there is no regulation there. And while the state holds back certain industries, others change at a speed that no institution can handle. Ultimately, the individual is left one-on-one with progress.
Two Traps Keeping Us in Place
Here is a paradox of modern times: every one of us has access in our pocket to knowledge that previous generations couldn’t even dream of. Any book, any lecture, any course from the world’s best specialist is just three clicks away. An AI chat can explain any concept, analyze any question, or translate any text. And yet, most people are getting poorer. Why?
The first trap is the illusion of accumulation. We hoard courses in our bookmarks, subscribe to channels, and save articles. In this accumulation, a feeling is born that we are already “in the process.” But a course added to your favorites is not knowledge. The more accessible a resource is, the less we value it. What can be obtained at any moment is postponed for later. And “later” never comes.
The second trap is cheap dopamine. Previously, pleasure required effort: to watch a new movie, you had to buy a ticket, travel to the cinema, and return home. It was an event. Today, you don’t even have to get off the couch. Online cinemas, social networks, video games—an endless stream of immediate reward for zero investment.
Learning is structured differently. It requires time, discipline, and delayed gratification. The dopamine doesn’t come immediately—first the effort, then the result. In a world where pleasure costs pennies and is available instantly, the brain chooses the easy path. This isn’t laziness in the everyday sense; it’s neurobiology.
Previously, life itself created urgency: no knowledge, no job, no food. Today, one can live in an illusion of well-being long enough not to notice their profession slowly evaporating. And one morning, a person will find that their position has been cut “due to restructuring,” while thousands of similar specialists with the same depreciated skills are standing in the same line for benefits.
The Choice That Has Already Been Made—Or Not Yet
It is impossible to stop what is happening. Technology does not ask for permission. But there is something you can choose: to be among those replaced by an algorithm, or among those who apply these algorithms. To use the new economy as a threat or as a tool.
On a practical level, this means that learning ceases to be an option and becomes a mandatory part of the week—like sleep or nutrition. At least 10–12 hours a week dedicated to mastering new tools, models, and approaches. Not “someday,” not starting from the New Year, but right now, this coming Monday, in your calendar.
This applies doubly to entrepreneurs. Managing a business based on intuition and trial and error means competing with yesterday’s tools against those who have already integrated AI agents into sales, marketing, and operations. The gap will only grow.
Right now, at this very minute, you have a concrete choice: return to what you were doing before reading this article, or open your calendar and schedule those first ten hours. The new economy won’t wait while you finish reading the next season.
The opinions expressed in this column are those of the author and may not reflect the views of the editorial team.
Business systematization expert and guest speaker at international business conferences; author of 4 books on business management.
