Why I believe the greatest danger of AI in the workplace may not be job replacement, but the gradual disappearance of opportunities to learn.
For the past few years, we have been asking the same question about artificial intelligence.
Will AI take our jobs?
I have discussed this question in different forms through my research, writing, and conversations about the future of technology.
But recently, I have started thinking about something that concerns me even more.
What happens when AI doesn’t eliminate an entire profession, but removes the work through which people learn that profession?
Imagine a company deciding that it no longer needs junior software developers because AI can generate much of the routine code.
The company retains experienced engineers to review the output, manage architecture, and solve complicated problems.
From a business perspective, this might appear to be an intelligent decision.
Expenses decrease. Productivity increases. Projects move faster.
But now imagine what happens five years later regarding AI and entry-level jobs.
Where will the company find its next generation of experienced engineers?
If nobody gets the opportunity to become a junior engineer, how will they eventually become a senior engineer?
I believe this question deserves far more attention than it is currently receiving.
And recent research suggests that the problem may already be emerging.
We Are Beginning to See the Warning Signs
On October 26, 2026, researchers at IIT Madras released findings from a white paper examining how AI is changing the structure of employment in India.
The paper, How AI Is Reconfiguring Work in India, challenges the idea that artificial intelligence will simply eliminate entire professions.
Instead, it argues that jobs consist of different tasks, and those tasks are affected differently by automation.
Some activities can be automated.
Others can be improved through AI assistance.
And certain responsibilities continue to require human judgment, experience, and accountability.
I find this distinction particularly important.
But the research also highlights another concern.
Entry-level employees often perform repetitive tasks that help them develop the experience required for more advanced responsibilities.
When AI automates those activities, the traditional pathway to professional expertise can become weaker.
This is not happening only in India.
Research from Stanford’s Digital Economy Lab, revised in August 2026, found that employment among workers aged 22–25 in AI-exposed occupations was approximately 19% below the level it would have reached if it had followed the trend of their less-exposed peers.
Importantly, the researchers did not find evidence of widespread, economy-wide job displacement. They also cautioned that their findings were descriptive rather than proof that AI caused the entire difference.
Nevertheless, the pattern raises an uncomfortable question.
Are we creating a future where experienced professionals remain valuable, but young professionals struggle to obtain the experience required to replace them?
The Tasks We Call Repetitive Are Often How People Learn
I want to share something from my own perspective.
My background is in electronics and communication engineering.
Later, my interests expanded into research, artificial intelligence, process excellence, business strategy, and entrepreneurship.
When I reflect on that journey, I recognize that professional understanding does not appear overnight.
It develops gradually.
You study concepts.
You experiment.
You make mistakes.
You discover that something which works perfectly in theory can behave differently in practice.
You investigate the reasons.
And slowly, you begin developing judgment.
That process matters.
Consider a junior software developer.
Writing relatively simple code may seem like a task that can easily be automated.
But while writing that code, the developer learns how applications are structured, how errors occur, how systems interact, and why certain decisions produce unexpected outcomes.
A junior accountant develops understanding through reconciliation, bookkeeping, and examining financial records.
A customer service representative learns through routine interactions before becoming capable of handling complex complaints.
A young researcher develops judgment by examining previous studies, checking evidence, and conducting basic analysis.
These activities may appear repetitive to an experienced professional.
But they are not necessarily meaningless.
Sometimes, the work that appears least valuable to a company is the work that creates the most valuable learning for an individual.
And I believe we are at risk of overlooking that distinction.
We Are Measuring Productivity but Ignoring the Cost of Lost Learning
As a Lean Six Sigma practitioner and someone who studies business processes, I understand why organizations want to eliminate repetitive work.
If a task can be completed more accurately, quickly, and economically through automation, there may be good reasons to automate it.
I support that objective.
But process excellence has also taught me that improving one part of a system does not guarantee that the entire system improves.
Consider a company employing ten junior engineers.
It introduces AI coding tools and discovers that five experienced engineers can now complete much of the same work.
Management reduces junior recruitment.
Its immediate financial results may improve.
But a hidden cost begins accumulating.
The company is no longer developing as many future engineers through real project experience.
I think of this as the training debt of AI adoption.
The organization enjoys today’s productivity gains while postponing the challenge of developing tomorrow’s workforce.
Like technical debt, this problem may remain invisible for years.
Eventually, however, it could become expensive.
Experienced professionals retire, move to competitors, change careers, or take leadership responsibilities.
The company needs replacements.
But the pipeline of people with practical experience has become weaker.
Now the organization must recruit experienced professionals from elsewhere, potentially at higher costs.
Of course, AI may also make training faster and more accessible. That is a genuine opportunity.
But if organizations simply remove junior positions without developing alternative learning pathways, I believe the long-term consequences could be serious.
There Is Another Problem: Who Will Evaluate the AI?
This is where the situation becomes particularly interesting.
We are increasingly telling young professionals that they should learn to supervise, evaluate, and correct AI-generated work.
I agree.
These are important capabilities.
But I want to ask something.
How can someone reliably judge the quality of work they have never learned to perform or understand?
Imagine asking a beginner to review AI-generated software architecture.
The system produces an impressive explanation.
The documentation looks professional.
The code appears well organized.
But somewhere inside the application is a subtle security vulnerability.
Would the beginner recognize it?
Perhaps, if they have received appropriate training.
But simply having access to an AI system does not automatically give someone the expertise to evaluate its output.
The same problem applies to finance, engineering, healthcare, legal research, and many other fields.
AI can produce convincing work.
But evaluating that work often requires experience, contextual understanding, and the ability to recognize unusual circumstances.
I believe we risk creating a strange contradiction.
We want humans to supervise increasingly capable AI systems while simultaneously automating the activities that help humans develop supervisory judgment.
That is not a problem that can be solved merely by writing better prompts.
It requires us to rethink professional education and workplace learning.
India Could Face a Particularly Difficult Transition
I am especially concerned about what this means for India.
Our country has a large population of young people who aspire to build careers in technology, finance, engineering, consulting, and professional services.
For decades, entry-level positions have provided an important pathway into these industries.
India’s IT and business process outsourcing sectors have also created opportunities for graduates from a wide range of educational backgrounds.
But AI is changing the economics of routine digital work.
Companies may need fewer people for certain activities.
Other jobs may become more complex.
New roles may emerge.
And the qualifications required for entry-level positions may rise.
A June 2026 study from the Institute of Labor Economics found a 14–15% relative decline in junior versus senior software developer vacancies in its analysis of U.S. hiring data after the introduction of ChatGPT.
It also found that employers were increasingly asking for greater experience within existing job titles.
These findings do not automatically translate into equivalent changes in India.
Our labor market, business conditions, wages, and technology adoption patterns are different.
But I believe India must examine this possibility carefully.
What happens when fresh graduates are expected to demonstrate professional experience before obtaining their first opportunity to develop it?
Universities may teach students how to use AI.
But companies will still need people capable of understanding real business problems.
I think this gap between academic preparation and professional expectations could become a major challenge.
I Don't Think the Answer Is to Stop Automation
There is something I want to make clear.
I am not suggesting that companies should preserve inefficient processes merely to provide employees with repetitive work.
That would be the wrong response.
If AI can eliminate unnecessary manual activities, we should explore those improvements.
But I believe the responsibility of organizations must evolve.
In the past, learning often happened naturally as part of ordinary work.
A beginner performed simple tasks, gradually encountered more difficult situations, and eventually gained enough experience to manage complex responsibilities.
AI could disrupt that progression.
So perhaps we need to design learning more deliberately.
A junior developer might begin by understanding and debugging AI-generated code instead of writing every function manually.
But they should also learn the underlying programming concepts and complete tasks independently.
A new analyst might use AI to prepare an initial report but remain responsible for verifying data, questioning assumptions, and explaining conclusions.
A trainee engineer could use AI simulations while also participating in real experiments and understanding why the simulated results may not match actual conditions.
The objective should not be to prevent young professionals from using AI.
It should be to ensure that using AI accelerates the development of expertise rather than becoming a substitute for developing it.
Founders and Business Leaders Have a Responsibility Here
As the founder of ReThynk AI, I believe this issue should influence how we think about future teams.
An AI-first organization should not necessarily mean an organization with the fewest possible human employees.
It should mean one that uses artificial intelligence intelligently.
There is a difference.
If AI allows a company to operate with a smaller team, that may create economic advantages.
But I would encourage founders to consider the long-term implications of their hiring decisions.
Are we developing people who can eventually take greater responsibility?
Are junior employees gaining meaningful experience?
Are we teaching them how to challenge AI outputs?
Or are we simply making them dependent on systems they do not fully understand?
These questions will become increasingly important as autonomous AI systems become more capable.
And there is an interesting warning from recent business developments.
Some organizations that reduced staffing while increasing reliance on AI have subsequently reconsidered those decisions.
In October, the Associated Press reported on employers restoring certain positions after discovering that AI systems required more human oversight and could not adequately replace institutional knowledge or judgment.
This does not prove that automation will fail across industries.
It demonstrates that companies can underestimate the responsibilities employees perform beyond their formal job descriptions.
I believe that is a valuable lesson. Beside, there is another valuable lesson for founders and entrepreneurs The darker days ahead: Why I Fear a Major Global Economic Slowdown in 2027!
We Need to Redefine What an Entry-Level Job Means
For years, companies have described entry-level positions as opportunities for people with limited professional experience.
But I think the definition may need to change.
Instead of treating junior employees primarily as a source of inexpensive labor for routine activities, organizations may increasingly need to treat entry-level roles as structured opportunities to develop professional capabilities.
That could include apprenticeships, supervised AI-assisted projects, practical assessments, and gradual exposure to increasingly complex decisions.
Universities and employers will need to cooperate more closely.
And young professionals will need to develop both technical knowledge and the ability to evaluate, communicate, and apply that knowledge.
This transition will not be easy.
It will require investment.
And not every organization will be willing to make it.
But I believe companies that develop these capabilities effectively could gain a significant long-term advantage.
Because when experienced talent becomes difficult to obtain, the ability to develop that talent internally becomes valuable.
Final Thoughts: We Must Not Automate Away the Next Generation of Experts
I remain optimistic about artificial intelligence.
I believe it will create extraordinary opportunities for researchers, entrepreneurs, students, engineers, and businesses.
And I expect many traditional jobs to evolve substantially.
But I also believe that every major technological transformation creates consequences that are not immediately visible.
The most obvious consequence of AI automation is that certain tasks can be completed with less human effort.
The less obvious consequence may be that fewer people get the opportunity to learn those tasks.
Over time, that could affect the development of expertise across entire professions.
This is why I think the conversation about AI and employment needs to move beyond counting how many jobs are created or eliminated.
We should also ask how people become capable of performing the jobs that remain.
Who will train the next generation of engineers?
Who will develop the next generation of financial analysts?
Who will prepare the next generation of researchers, managers, and technical leaders?
And what happens if the traditional opportunities through which they gained experience gradually disappear?
A society that automates routine work but fails to redesign how people learn may eventually discover that it has made work more efficient while making expertise harder to develop.
That is a future I believe we should avoid.
Because the real success of artificial intelligence should not be measured only by how much human work it can replace.
It should also be measured by how much human capability it helps us develop.
— Jaideep Parashar
Founder & Director, ReThynk AI Innovation and Research Pvt. Ltd.
Profile: LinkedIn.com