The age of AI has arrived faster than even its evangelists anticipated. And yet, in most discussions about AI’s impact on work, we hear a strangely narrow claim: that it is entry-level and junior knowledge workers who are most at risk.
Why juniors?
Because AI substitutes execution before it substitutes judgment.
For decades, the knowledge economy has run on a quiet apprenticeship model. Entry-level professionals – in advertising, consulting, law, finance, medicine, management – begin by doing. They write drafts, crunch numbers, build decks, research case law, prepare media plans, construct financial models. Their work is corrected, reshaped and sometimes dismantled by seniors. Over time, something invisible accumulates: judgment. Taste. Context. Ethical instinct. Nous.
Execution is the training ground for wisdom.
But AI now executes.
This is the structural rupture. When AI can produce what juniors used to produce – faster and often more comprehensively – the apprenticeship ladder begins to collapse. If the first rung disappears, how does one climb to the second?
This is not a minor labour-market shift. It is a civilisational design flaw in the making.
In India, where the median age is under 30 and millions of graduates enter the workforce annually, the contraction of entry-level learning roles could be socially destabilising. In Europe, graduate underemployment is already politically fraught. In Japan, an aging society turns to automation out of necessity. In India, a young society risks exclusion at the point of entry.
The problem is not AI alone. It is that professional education was designed for a world in which execution preceded judgment.
We need a root-and-branch redesign of professional training around what I would call the Co-Agency Model of Professional Education.
Co-Agency assumes that the professional of the future will never work alone. She will always operate in partnership with AI systems. The question is not whether AI assists – it already does. The question is whether we systematically train humans to lead that partnership.
Under the Co-Agency Model, students are immersed in dynamic AI-generated environments – business simulations, legal disputes, medical scenarios, creative campaigns – where AI produces options, drafts and analyses in real time. But the student must:
- Frame the right prompts
- Interrogate assumptions
- Detect hallucinations or bias
- Apply ethical filters
- Decide under ambiguity
- Justify her reasoning
She is evaluated not merely on outputs, but on how she exercises judgment within a human-AI system.
Faculty, supported by advanced AI analytics, map patterns of decision-making across cohorts. They identify where students over-delegate to machines, where risk perception is flawed, where moral reasoning is thin. Interventions are targeted. The classroom becomes a laboratory of judgment, not a factory of notes.
We are beginning to see early signs of such shifts. At Indian Institute of Technology, Madras, AI research and applied industry collaboration are increasingly integrated. Students engage with real-world deployment challenges, learning not just to build AI, but to operate with it in live contexts. Globally, law and medical schools are introducing AI tools into training – not to replace professional reasoning, but to teach students how to supervise and critique machine outputs.
These are still incremental adaptations. The Co-Agency Model calls for systemic redesign.
Its ambition is bold: to produce AI-native decision-makers from day one — professionals who can operate at the level of context, systems thinking, ethics and orchestration rather than mere execution.
This will flatten hierarchies. It will compress career ladders. It will demand more from seniors and more from students. But it may also unlock earlier entrepreneurial confidence and creative self-direction.
If AI handles scale, repetition and operational complexity, then education must pivot decisively toward cultivating judgment, imagination and moral courage.
The future professional is not a faster worker.
She is a wiser orchestrator in a system of shared intelligence.
If we fail to redesign education around co-agency, we risk training millions for roles that algorithms will quietly absorb. If we succeed, we may create a generation capable of working with intelligent systems not as subordinates, nor as rivals, but as conscious partners in shaping the world.
The real question is not whether AI will transform work.
It is whether education will transform fast enough.
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