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Every technological revolution brings with it the promise of progress. Artificial intelligence, it is said, will free humans from repetitive and boring tasks, allowing them to focus on more creative, strategic, "value-added" activities. But if we look carefully at what is happening in many sectors, the reality is more complex—and in some cases, even the opposite.
Today, AI is not automatingjust simple jobs. It is starting to take on the more skilled, more creative, more cognitively demanding ones. And what's left for humans, too often, is a secondary, executive, impoverished role. In a word: demoted.
When AI rises in rank, humans fall
Think of a copywriter, once responsible for devising advertising campaigns, finding the right word, and sculpting a brand's identity. Today, in many agencies, the job has become: "ask ChatGPT for a title," "choose the least bad version," "correct the tone."
A radiologist? More and more often, they simply validate what an algorithm has already pre-analyzed. A professional translator? They post-edit drafts generated by neural systems.
Paradoxically, it's no longer humans who delegate the simplest work to machines. It is the machine that performs the noble part of the process, leaving the verification, supervision, and correction to humans—often with much narrower margins of action.
The new job: intelligent machine controllers
These are not just a few isolated examples. A whole economy of "interface" roles is emerging between AI and the real world:
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Prompt engineer: someone who formulates the right requests to AI, often trying again and again until the output is acceptable.
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Data labeler: manually annotates large amounts of data to train models Automatic.
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Content moderator: Filters toxic or inaccurate AI-generated content.
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AI babysitter: Checks that the algorithm is "behaving well," without glaring errors or obvious biases.
Many of these jobs are repetitive, poorly paid, and prone to burnout. They are essential to the functioning of artificial intelligence, but often under-recognized and rarely rewarding.
The illusion of technological liberation
We've been told a narrative in which AI removes the burden of automation from humans. But in practice, the eliminated tasks aren't necessarily the least interesting. In reality, AI is very good at precisely those tasks.Complex tasks where patterns exist: writing texts, analyzing data, designing strategies. It's not (yet) so effective at navigating ambiguous, relational, and contextual environments.
But for an employer, it's more convenient to automate the core skills of an experienced employee and leave the entire process to a junior (or external figure). In other words: don't replace effort, but value.
When progress devalues competence
This process has profound consequences:
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Loss of meaning in work: many professionals no longer recognize themselves in their role.
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Depletion of skills: If AI does everything, who will learn to write well, diagnose thoroughly, and design solutions?
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Professional stagnation: If AI occupies the "highest" roles, it becomes difficult to advance from the bottom.
And finally, a cultural risk: if we entrust all the difficult decisions, all the merit assessments, everything that requires sensitivity or experience to the machine... what's left for human responsibility?
Escaping Demotion: How to Really Change the Rules of the Game
The risk is not just losing "jobs," but losing the centrality of human labor in high-value processes. To counter this trend, radical actions are needed on three distinct levels: personal, corporate, and systemic.
1. On a personal level: Stop chasing AI, start leading it
Many professionals are trying to avoid being replaced by learning to use AI. But using AI better than others isn't enough if your role is still defined as "secondary to the algorithm."
🔧 Concrete solution:
Choose areas where AI has structural limitations—ambiguity, human context, responsibility. Don't become a "good AI user," but problem owner, capable of integrating tools with vision.
👉 Example: a UX designer must not only use ChatGPT to create wireframes faster, but rethink the user experience taking into account what AI cannot know.
2. At the corporate level: avoiding the "head-cutting" mistake
Many companies see AI as a way to cut down on heads: fewer copywriters, fewer analysts, fewer middle managers. The risk? Automate skills that are no longer even recognized.
🔧 Concrete solution:
Incorporate AI into processes without dismantling human know-how. AI can be the first level, but we need a second, human level that has room to correct, decide, and learn.
👉 Example: Instead of replacing data analysts with automatic dashboards, create roles that read that data critically, suggest new questions, identify anomalies.
3. At the system level: regulate the use of AI not only for privacy, but for the quality of work
AI regulations focus on transparency, bias, and data protection. But we also need a employment regulation that imposes minimum quality standards when AI enters the workplace.
🔧 Concrete solution:
Create contractual and training constraints: every AI implementation that modifies work processes should include:
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audit the residual value left to people
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Mandatory internal retraining programs
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Contractual recognition of newly created roles (don't leave them "unclassified" or precarious)
👉 Example: if a call center automates 80% of interactions, the remaining workers should not be evaluated on the same quantitative metrics, but reoriented towards complex customer care with an appropriate contract.
🎯 And above all: the obligation to guarantee understandability and traceability of automated decision-making processes!
👉 Why?
If key decisions are delegated to an opaque model—and humans are limited to overseeing outcomes they can neither explain nor predict—we are eliminating expertise and vision.
Solving a problem today with an AI decision is useless if we don't know why that solution worked. This creates a systemic fragility: no preparation for the unexpected, no strategy for the future.
👉 Example: If an algorithm decides which candidates to hire or which customers to serve first, but it's unclear on what basis it "chose," the company risks serious distortions in its processes—which no one will be able to correct when the context changes.
In short:
Using AI, we could solve the problem now, but without knowing the details or the prospects for unforeseen future events
The real question isn't whether AI will steal our jobs
Perhaps the real risk of artificial intelligence isn't that it will steal our jobs, but that it will leave us doing only the worst parts of our old jobs.
The real danger isn't just being "replaced" by AI, but being cut off from the meaning of work: no longer knowing why decisions are made, no longer being able to learn from mistakes, no longer seeing the bigger picture.
Exiting demotion means taking back control over the process, not just the result.
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