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Humans have always migrated not only in physical scenes, but also in ways to work and think. Every major technical revolution has demanded any migration: from the field to the factory, from the muscle to the machine, from the analog habits to the digital anxiety. These shifts did not just change what we did. They gave a new look on how we praised us and what we believe is to make us valuable.
A clear example of technical migration comes from the early 20th century. In 1890, more than 13,000 companies in the United States built horse -made vehicles. By 1920, less than 100 remained. In the same period of a generation, a whole industry collapsed. Microsoft’s Blog status The day the horse washed away from his job Described, it was not just about transportation, it was about the migration of millions of workers, the elimination of trade, the restoration of the city’s life and the widespread capacity of continental transportation. Technological development, when it comes, does not ask for permission.
Today, as AI grows more, we are entering the time of academic migration when humans have to move again. However, this time is less physical and more mental to be displaced: machines away from tasks are gaining expertise, and toward domains where human creativity, moral decisions and emotional insights are essential.
From the Industrial Revolution to the Digital Office, history is full of dynamic migration through machinery. Everyone needs new skills, new organizations and new statements on what their cooperation means. Everyone made new winners and left the others behind.
Freeming Shift: Ibm’s “Ulema Age”
In October 2015 A Gartner Industry ConferenceIBM CEO Guinea Rometi announced its launch in public, which the company said The scientific period. It was more than a smart marketing campaign. This was a new explanation in the strategic direction and, with the argument, the rest of the tech industry had to flare up a signal that a new phase of computing has come.
While the last decades was created by the programming systems based on the rules written by human software engineers, the academic period will be explained by systems that can learn, adapt and improve over time. This system is strengthened by machine learning (ML) and natural language processing (NLP), it will not be clearly stated what to do. They will guess, combine and talk.
At the center of this vision was IBM Watson, who had already made headlines to defeat Human Champions in 2011 In danger! But Watson’s real promise was not about winning the quiz shows. Instead, it was helping doctors suggesting treatment of thousands of clinical trials, or helping lawyers who analyze the wider bodies of the case. IBM laid Watson not as an alternative to experts, but as an amplifier of human intelligence, as the first academic co -pilot.
This shielding change was important. Unlike the tech -time, which emphasized automation and performance, the academic Era emphasized the partnership. The IBM spoke of “enhanced intelligence” instead of “artificial intelligence”, not as a partner to these new systems.
But the vision was a bit deep: an identity that academic wages, long -term white -collar professional class symbols, was not safe from automation. Just as the steam engine displaced physical labor, so will start to cross the academic computing domains that once thought about humans specifically: language, diagnosis and decision.
The IBM’s announcement was hopeful and calm. He imagined a future where humans can work more with the help of machines. It also pointed out in the future where value will need to be migrated once again, this time in the domains where machines are still struggling-such as meaning, emotional resonance and moral reasoning.
At that time, the announcement of the academic era was significantly seen, yet very few people realized its long -term implications. It was a formal declaration of the next great migration. Not one body, but of the brain. It indicated the change in the region, and a new journey that will test not only our abilities, but our identity.
First Great Migration: From Field to Factory
Now we should briefly consider the great academic migration and how it is standardized in human history, we must briefly consider the first migration. From the rise of factories to the modern workplace to digitalization in the industrial revolution, every major innovation has demanded a change in skills, institutions and our assumptions that its cooperation means.
The industrial revolution, which began in the late 18th century, made the first great migration of human wages in new ways to operate widespread work. Steam strength, mechanization and the rise of the factory system have drawn millions of people from rural agricultural life to crowded, industrial cities. Who had ever been a local, seasonal and physical laborer, had become organized, expertise and disciplined with productivity as a driving force.
This transfer has not only changed the place where people worked. It changed to who they were. The blacksmith or the mochi of the village went into new roles and became a coag in a wide industrial machine. Time watches, shift work and performance logic have begun to renew human partnerships. The entire generation had to learn new skills, embrace new routines and accept new rankings. It was not just a laborer who emigrated, it was identity.
Similarly, the important thing is that the institutions also had to migrate. Public education system was expanded to manufacture literate industrial manpower. Governments have put labor laws into new economic conditions. Unions come out. Cities increase rapidly, often without infrastructure. It was dirty, uneven and painful. It also marked the beginning of a modern world, in the form of – and rapidly the form of machines.
This migration once again made a model: Demanded modern technology, and people and society need to be adopted. This adaptation slowly – or sometimes violently – until a new balance eventually emerges. But each wave has asked more from us. Our bodies are needed for the industrial revolution. Next our minds will be needed.
If the industrial revolution demanded our bodies, the digital revolution demanded new minds. Beginning in the mid -20th century and faster in the 1980s and 90s, computing technologies have once again changed humanitarian work. This time, repeated mechanical tasks were rapidly replaced with information processing and symbolic manipulation.
Which is sometimes called the Information Age, the clerks became data analysts and the designers became digital architects. Organizers, engineers and even artists started working with pixels and codes instead of paper and pen. From the work factory floor, the office tower and finally went to the screen in our pocket. The work of knowledge became not only dominant, but also desire. Computer and spreadsheets became a new economic poem and became a balcony.
I saw this first hand at the beginning of my career when working as a software engineer in Health Packard. Several new-shaped MBA Graduates arrived with HP-branded Victra PC and Lotus 1-2-3 spreadset software. It was seemingly when data analysts began to take advantage of cost -advantage analysis, changing the enterprise operational performance.
The migration was less painful than the field to the factory, but no less important. It renews productivity in academic terms: memory, organization, summary. It also brought new forms of inequality between people who could master the digital system and among those who were left behind. And, once again, the institutions entered to maintain speed. Schools re -worked for the “21st Century skills”. Companies reorganized information flow using techniques such as “Business Process Regerating”. The identity also moved again, this time from the worker to the worker.
Now, in the middle of the third decade of 21stay The century, even the knowledge work is becoming automatic, and white -collar workers can change the climate. The next migration has already begun.
Yet the deepest migration
We have moved our hard work to fields, factory and fiber optics. Every time, we have shielded. It has often been uneven and sometimes painful, but we have turned into a new routine, a new balance. However, now the ongoing academic migration is the opposite. It’s not just how we work. It challenges that we have long believed that it makes us irreparable: our rational mind.
Since the AI ​​grows more worthy, we have to shift again. Not towards strict skills, but towards deep things that became human powers, including creativity, morality, sympathy, meaning and even spirituality. This is still the deepest migration because this time, it’s not just about avoiding change. It is about to discover who we are from creating and understanding the true nature of our value.
Rapid change, compressed adaptation
The timeline for every technical migration has also intensified dramatically. The Industrial Revolution came to light in a century, allowing for generation to be adapted. The digital revolution compressed this timeline in a few decades. Some workers started their career with a retired paper files and cloud databases. Now, the next migration has been happening in only years. For example, the largest language model (LLM) went to workplace tools in less than five years with educational projects.
William Bridges noted in the 2003 amendment.Transfer. The speed of change is now much faster than in 2003, which makes it even more important.
This acceleration reflects not only in AI software but also in basic hardware. In the digital revolution, the dominant element of computing was CPU, which implemented serial instructions based on the rules of the software engineer clearly. Now, the dominant computing factor is GPU, which follows the instructions parallel and learns from data rather than rules. Providing parallel to work provides a clear speed of computing. It is no coincidence that GPU leading developer, Nvidia, describes it as “fast computing”.
Excessive migration
Transfers that are once produced in generations are now taking place within the same carrier, or even within a decade. This significant change not only demands new skills, but also makes us a basic overview of it. Unlike previous technical shifts, we cannot easily learn new tools or adopt new routines. We must move to regions where our individually human characteristics of creativity, moral decisions and meaning become our explanatory power. The challenge to us is not just technical adaptation, but a new explanation of existence.
As we specialize in AI systems that we once understood as human tasks, we find ourselves on a fast journey to discover that it is really above the automation: the essence of being a human being is not just our special domain.
