Thank you dear subscribers, we are overwhelmed with your response.
Your Turn is a unique section from ThePrint featuring points of view from its subscribers. If you are a subscriber, have a point of view, please send it to us. If not, do subscribe here: https://theprint.in/subscribe/
Asking which jobs AI will replace misses the point. AI will touch almost every profession while eliminating very few outright. Its real impact is to raise productivity so sharply that fewer people are needed in each field. The choice is simple: use AI to deliver far more value, or compete in a shrinking market against those who do. What matters now is learning how to become an order of magnitude more effective in the emerging AI world order.
The scale of this shift is difficult to demonstrate in the abstract. It is not something that convinces easily on paper. You only begin to understand it by using AI seriously in your own work. Once you do, the idea of an order-of-magnitude improvement in productivity stops sounding like hype and starts sounding like lived experience.
Four ways to use AI — and why each can be an order-of-magnitude shift
People talk about “using AI” as if it were one thing. It isn’t. AI plays very different roles depending on how it is used, and each role removes a different bottleneck in human work. That is why the gains are not marginal.
First, AI as a teacher.
When you are learning something new, the biggest constraint is not intelligence, but time. Traditionally, learning meant searching, reading, getting stuck, waiting, and often giving up. AI collapses this entire cycle. You can ask naïve questions without cost, request explanations at exactly the level you need, and iterate until clarity emerges.
One way to mitigate this risk is through triangulation—using a second, distinct AI model to challenge the explanations of the first. What once took weeks of fragmented effort can now happen in hours. When the cost of learning drops this sharply, the speed at which you can enter new domains increases by an order of magnitude.
Second, AI as a colleague.
Here the constraint is not knowledge, but cognitive bandwidth. Humans are good at judgment but poor at holding multiple alternatives in their head at once. AI removes that limitation. More importantly, it removes the scarcity of thoughtful engagement. Finding a human colleague who can patiently explore half-formed ideas, challenge assumptions, and iterate without cost is difficult. AI makes that kind of intellectual companionship effectively unlimited.
It generates options, counterarguments, edge cases, and variations instantly. You are no longer limited by how many drafts you can write or how many possibilities you can explore. Thinking becomes less about producing and more about choosing. When exploration becomes cheap and judgment becomes the bottleneck, output scales dramatically. Here too, triangulation matters: treating AI not as a single source of truth, but as a fast way to cross-check, compare, and synthesise information across multiple perspectives.
Third, AI as an intern.
Much of professional work is not hard, just time-consuming. First drafts, summaries, clean-ups, translations, reformatting, repetition. These tasks previously consumed hours because humans had to do them sequentially. AI does them in parallel, instantly, and without fatigue. When a large fraction of low-judgment work disappears from your day, the effective output of a single person multiplies. This is not a small efficiency gain. It is the removal of an entire layer of effort.
Crucially, this mode is how we must now train junior staff. Instead of asking a junior to write the first draft (which AI can do), the expert should assign the junior to supervise the AI. The junior prompts the system, validates the output, checks for hallucinations, and refines the tone. This forces the junior to practice judgment—spotting errors and defining quality—from day one, rather than spending their first years merely on execution.
Fourth, AI as “Google on steroids.”
A large share of professional time is quietly consumed by searching—looking for the right document, recalling prior work, scanning articles, stitching together scattered information. Traditional search tools return links; they still leave the work of reading, filtering, and synthesizing to you. AI collapses this entire process. It retrieves, summarises, and connects information in one step.
What once required dozens of small, interrupted actions now happens in a single interaction. When search and synthesis become nearly frictionless, decision cycles shorten dramatically. Over time, the cumulative effect of this compression easily reaches an order-of-magnitude difference in how much meaningful work one person can get done.
The point is that when these modes stack together, they remove multiple constraints at once: learning time, cognitive load, execution effort, and search friction. When several bottlenecks vanish simultaneously, productivity does not improve linearly. It jumps.
This does not mean that any single use of AI guarantees a 10× improvement.
And one way not to use AI: as an oracle.
Oracle mode is when AI is treated as a source of final answers rather than a tool for thinking. It feels efficient, but it quietly creates fragility. AI can hallucinate—producing fluent but false outputs—and blind trust replaces judgment with errors that compound over time. This is not automation; it is abdication.
The oracle model fails not just because it outsources judgment, but because AI is not repeatable. Its outputs vary by design and only remain reliable when a human evaluates and decides. Remove the human and the system becomes fragile; keep the human in the loop and variability becomes manageable. The mistake is not using AI, but pretending it does not need judgment.
The conclusion is straightforward.
The most important skill in the AI era is not a job or a tool, but the ability to work with AI while retaining judgment. Knowing when to use it, when to challenge it, and when to ignore it. AI rewards neither avoidance nor blind trust. It rewards those who convert machine intelligence into human leverage through judgment and accountability.
Jobs will change, titles will blur, and workflows will be rewritten. For experienced professionals, the scarce capability will be the ability to command powerful systems without surrendering judgment to them. This is no longer a niche skill; it is what separates those who scale from those who fade. There will still be demand for humans in control, but far fewer of them. The choice is simple: pilot or passenger.
For interns and those entering the workforce, fluency with AI will not be an advantage—it will be the baseline expectation. Entry-level work will no longer be defined by raw execution, because AI can already do much of that. Instead, young professionals will be expected to supervise AI systems, validate outputs, catch errors, and exercise judgment from the very beginning. This has unavoidable implications for education.
Yet today, many institutions still treat the use of AI in learning with suspicion, as if it were primarily a shortcut or a form of cheating. That stance is increasingly misaligned with reality. If AI fluency is becoming a basic workplace requirement, then learning how to use AI thoughtfully and responsibly cannot remain outside the classroom. The challenge for education is no longer whether students will use AI, but how to teach them to do so while retaining judgment and accountability.
These pieces are being published as they have been received – they have not been edited/fact-checked by ThePrint.