[Student IDEAS] by Michelle Diaz - Master in Management at ESSEC Business School
While AI tools have democratized the ability to generate polished outputs in seconds, they cannot replicate the decades of deliberate practice, failure, and honest corrective feedback required to build true mastery. Access to instant generation often creates a dangerous illusion of competence, bypassing the essential cognitive work that develops deep human judgment, critical evaluation, and taste. Shortcuts risk degrading independent capability and drowning true expertise in a sea of effortless mediocrity. Ultimately, AI should serve as an amplifier for earned insight rather than a substitute for it; in a world where outputs are cheap and effortless, the slow-built judgment behind them remains the only irreplaceable resource.
---
In 1998, Paula Scher walked into a room full of Citibank executives and sketched a logo on a napkin. The arc over the t. The red curve borrowed from Travelers Group's umbrella. Done. The meeting had barely started. The sketch cost $1.5 million.
The room, predictably, was not pleased. Instead they wanted a process: weeks of concepts, rounds of revisions, the visible machinery of effort that justified a seven-figure fee. Instead, they got a second. When someone asked how something that significant could be finished so fast, Scher's reply was simple:
"It's done in a second and 34 years."1
Therein lies my argument. The second is the part you can see. The 34 years are invisible: accumulated through failure, repetition, correction, and the ever slow development of judgment that no shortcut produces. Citibank wasn't paying for the napkin. It was paying for everything that made the napkin possible.
Now, consider what exactly happens in a society where anyone and everyone can generate a logo in seconds. AI tools compress the distance between an idea and a polished output, between a question and a researched answer, between zero competence and the convincing appearance of one. The second has been democratized. Unlike the 34 years.
Historically, mastery is a word people use freely, but rarely with precision. It gets attached to six-month habits and morning routines and anyone who has gotten noticeably good at something in a short time. Used this way, it’s more motivational than meaningful. So before arguing about what AI does to mastery, let’s be honest about what it actually is—and what it isn’t.
Figure 1 maps the distinction:
The research on expertise isn’t romantic. It doesn't reach for words like passion or gift. Scholars in the field describe it as a sequence: motivated engagement, clear objectives, focused repetition, feedback that arrives in real time, and standards that must be demonstrably met before you move forward2. Not when you feel ready. When the standard is met. Only then does the loop close.
What separates this from mere repetition is the quality of honesty inside it. A musician running scales without listening critically to the sound they're making isn't building mastery. Sure, they're accruing hours. But the loop only works when it's truthful.
And that truthfulness? It’s uncomfortable. Painfully uncomfortable. Genuinely corrective feedback tells you exactly what you got wrong. It asks you to sit with inadequacy long enough for your understanding of the problem to change. Most people find this, over time, intolerable. The ones who don't, tend to describe the experience not as enjoyment but as compulsion—something that pulled them back even when the results were painfully discouraging.
Phyllis Elago, a trained classical musician, now also a writer and translator, describes it with the precision of someone who has lived inside both disciplines:
"By doing it again and again and observing your results each time, you learn how to connect to your body and your brain so that you can do whatever you want at will. Then it becomes easy. Until then, if you are still suffering, the audience will suffer with you."
See, the thing is, research shows that the act of learning leaves a physical imprint on the brain, not simply an experiential one. Consider the landmark study of taxi drivers. The study found measurable growth in the part of the brain responsible for spatial memory, with volume increasing in direct proportion to years of experience3. In other words, expertise doesn’t accumulate as a larger catalog of techniques filed away for later use.
A chess grandmaster doesn’t calculate more moves than a novice; they simply look at the board differently from the first glance. Similarly, a seasoned musician doesn’t rely on trial and error to find key combinations; they already know which keys tend to work better with which. Decades of honest repetition and learning don’t just make you faster, it changes the way you look and what you see.
One can read about mastery, cite it, admire it from a safe distance. But it has to be lived through. The repetition, the failure, the small corrections, the incremental and often invisible progress. None of it is merely the road to the capability; it is the capability, being assembled slowly, in the only way it ever could be. It's easy enough to watch a seasoned pianist play Beethoven's Sonata No. 29 in B-flat major. Harder, to replicate it by simply knowing how to read the notes.
Now, to be very clear: none of this is an argument against AI tools. AI compresses the zero to one journey in specific, real, and when used consciously, often genuinely useful ways.
For example, a student learning to code gets instant feedback on errors that once required a teacher or a patient colleague. Or a designer iterates in minutes what once took days. And a junior analyst drafts in an hour what once consumed a week. These are not trivial gains and dismissing them wholesale is its own kind of intellectual dishonesty.
However, there is a difference between the compression of a process and the offloading of the cognitive work within the process. And therein lies the issue.
A recent study at Stanford’s Human-Centred AI institute put it plainly: students performed better on tasks when AI tools were made available to them. And once the tools were removed, the advantage disappeared entirely. More troubling still, the students who had grown accustomed to and reliant on the AI assistance—and then lost access—performed dramatically worse than their peers who had never used AI tools at all. In one measure of creative performance, they worsened by four times4. In essence, the access to AI tools quietly, but surely degraded the operator.
The pattern holds beyond the classroom. Indeed, the Stanford 2026 AI Index found that engineers who leaned on AI for learning picked up “learning penalties”— measurable slowdowns in their independent development over time5. And open-source developers using AI assistance became, on certain complex tasks, slower than their counterparts working without the assistance. So the ceiling, it turns out, is not access to tools, but rather the judgement to use them well. And judgement is precisely what the shortcuts are bypassing.
Kateryna Sazanova, an Emmy award-winning director and producer with well over twelve years across film, television, and digital production, describes it as a kind of readiness that only truly accumulates through repetition:
“Working on set is very concrete and tangible, with lots of moving parts, and everyone needs to be fully ‘on’,” she says. “Such experience cannot be replaced by reading or talking about it.”
Paula Scher didn't sketch that logo out of sheer talent or luck. She sketched it because she had internalised decades of failed attempts, difficult client briefs, wrong directions, and painful corrections. It’s the accumulation of all those experiences that gives rise to taste, judgement, or that almost magical ability to know immediately what’s off. That kind of skill isn't stored in a file. It doesn't wait for the right moment to be needed. It either exists, built slowly through the loop, or it doesn't. An AI that produces a logo in a second doesn't contain Scher's 34 years. It contains a statistical average of everyone else's.
As such, this is the more subtle danger, which goes largely unremarked in conversations around AI’s impact on work. Indeed, the concern most often voiced is displacement—that the tools will themselves take the jobs. But the quieter, more consequential concern should be substitution: people will use the tools to produce outputs they don’t fully understand, will have a hard time defending themselves, in domains they haven’t earned the right to navigate. A medical student who uses AI to arrive at a diagnosis without developing clinical reasoning isn’t a better doctor. Similarly, an architect who uses AI to generate a hundred options in a few hours without the taste to evaluate them isn’t a better architect. These are simply people holding and presenting outputs they can’t interrogate, in fields whose depth they haven’t entered.
The seconds are now on offer to everyone, and that alone really is extraordinary.
But what hasn’t changed is what those 34 years actually add up to—or what quietly slips away when a whole generation decides the seconds are good enough and stops reaching for anything more.
Scale this up. What happens to one student, designer, or engineer compounds when it happens across whole cohorts, across industries, institutions, generations of people entering professions having produced outputs they were never required to fully understand. The individual cost of skipping the loop is a quieter kind of incompetence. The collective cost is something harder to name, slower to notice, and potentially irreversible.
The people closest to the technology are already sensing it. The EY 2025 Work Reimagined Survey put it plainly: a significant portion of UK employees worry that AI is doing something to their capabilities they can't quite name; that "an overreliance on AI could erode their skills and expertise"6. These people aren’t outsiders looking in, nor are they critics with an agenda. Instead, these are people using the tools, seeing something happen to their own capabilities in a way that’s hard to articulate.
Now, the data, when it catches up, tends to confirm this experience. AI adoption at work has grown rapidly: 44% of UK employees now report using it at work7. But a closer inspection of the distribution is telling. Fewer than 5% are “using AI to fundamentally transform the way they work8.” The rest are producing faster, smoother, more polished versions of work whose underlying quality hasn’t moved—reiterating that access was never the ceiling. Judgement is; and that judgement is one thing the adoption statistics haven’t quite measured.
Navada Currie has spent a decade in design: across agencies, non-profits, freelance work, and nationally recognised campaigns in Australia, among them the World's Greatest Shave, which has raised over fifteen million dollars. She can see the difference:
“People who've taken the long road tend to bring a deeper level of understanding to their work. They're not starting from zero each time; they're drawing on years of practice, mistakes, references, and problem-solving. That depth becomes its own kind of shortcut."
According to her, the thing that shows up isn’t polish, but judgement. Shortcut-driven work can solve the more visible parts of a project brief, but it’ll struggle to survive the question of why:
"AI-generated branding can produce something decent-looking very quickly, but it often lands on the first obvious solution. A skilled designer will push beyond that — shaping something that connects to the brand's wider story."
Kateryna Sazanova, whose twelve years in film production and 2025 Emmy Award give her a particular vantage point on this, put it plainly:
“Now we see a flood of bad content. No matter what tools you use, it takes craft and mastery to produce a good story that will resonate, inspire, connect, give a new perspective, motivate for action and stay with the audience for a long time.”
Once you zoom out, the irony is kind of staggering. AI has made mediocrity effortless and excellence harder to see. So when every student produces a polished essay, or a junior employee a competent presentation, or a designer a slick visual, the signal that once clearly separated deep capability from surface performance gets lost. And the noise rises. The people who actually made the hard journey become much harder to identify, and organisations can no longer tell the difference.
But most work requires a human touchpoint, be it a presentation, a defense, or a live critique; the gap reveals itself eventually. The tools didn’t create this dilemma. They’ve just made it far easier to avoid confronting, until the worst possible time.
Consider what the loop actually produces, beyond technical capability. It produces failure literacy: the ability to recognise when something is going wrong before it has fully gone wrong. It produces taste, which is just a fancy word for the accumulated weight of every bad decision made and eventually, corrected. Isn’t that the kind of confidence that doesn’t normally announce itself? Because it’s built on evidence rather than outputs? These things aren’t exactly transferable. Not exactly teachable in the conventional way. They accrue slowly through the loop, showing up in how a person moves through a problem and not simply through what a person produces. Hence why professional schools across disciplines don't just teach theory; medicine mandates clinical rotations, law builds moot courts, architecture runs studio critique, business runs live consulting cases. The form changes. The underlying principle doesn't.
Now put that person in a room with AI. They can generate a hundred options and immediately know which ones are worth pursuing. They can work from an AI draft and understand why something is almost right, but not quite. They can use the tool at speed and trust their own judgment about when the tool is failing them. The 34 years don't become irrelevant in that room. They become the only thing that matters. This isn't a case against AI as a tool. It's a case for sequencing—for building enough to know what you're looking at before you ask a machine to generate more of it.
I want to be very careful. The people who skipped the loop aren't without ability. They can produce. What they struggle to do is evaluate, interrogate, and redirect—the work that happens after the output exists. And that is precisely the work that AI can’t do on their behalf, because that’s the work that requires a human to have a formed opinion about what excellence actually looks like.
In a world where the output has been democratized, judgment is the scarce resource. It always was. It's just easier to see now.
Let’s return to that room with Scher for the moment: the executives, the napkin, the red arc over the t, the sketch that took a mere second, and the room that couldn’t see the 34 years standing behind it.
What they were looking at, without knowing it, was the product of a process so incredibly long and internal that it practically dissolved into invisibility: the failed pitches, the wrong directions, the arduous accumulation of knowing what doesn’t quite work and why—all of that compressed into a single, frankly unremarkable moment that only looked effortless because of everything it contained.
We live in a culture that has essentially built something that can replicate that moment: the sketch, draft, deck, diagnosis; generated, polished, delivered. Fast and clean and competent. And increasingly indistinguishable on the surface from the work of someone who’s spent years earning the right to produce something.
But surfaces are not the entire story. Frankly, when are they ever?
The question I want to leave you with isn’t whether to use the tools. Use them. The question is whether you’re building something underneath the tools’ usage; something that would survive if the tools disappeared tomorrow. Something that’s yours, assembled slowly through an iterative loop, through the long and often thankless distance between not knowing and knowing deeply.
That distance has a name. And I’d argue it’s worth every step.
[1] Paula Scher, Abstract: The Art of Design, Season 1, Episode 6, dir. Richard Press, Netflix, 2017.
[2] McGaghie, W.C., Issenberg, S.B., Cohen, E.R., Barsuk, J.H., & Wayne, D.B. (2011). Does simulation-based medical education with deliberate practice yield better results than traditional clinical education? A meta-analytic comparative review of the evidence. Academic Medicine, 86(6), 706–711. / Mastery learning framework: see also Bloom, B.S. (1968). Learning for mastery. Evaluation Comment, 1(2), 1–12.
[3] Maguire, E.A., Gadian, D.G., Johnsrude, I.S., Good, C.D., Ashburner, J., Frackowiak, R.S.J., & Frith, C.D. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences, 97(8), 4398–4403. https://www.pnas.org/doi/10.1073/pnas.070039597
[4] Lichand, G. et al., cited in: Stanford Human-Centred AI, AI Challenges Core Assumptions in Education, February 2026. https://hai.stanford.edu/news/ai-challenges-core-assumptions-in-education
[5] AI Index Steering Committee, Artificial Intelligence Index Report 2026, Stanford University, April 2026. https://www.starkinsider.com/2026/04/stanford-2026-ai-index-report.html
[6] EY 2025 Work Reimagined Survey, December 2025. https://www.ey.com/en_uk/newsroom/2025/12/uk-employers-miss-40-percentage-ai-gains-talent-gaps
[7] EY, "UK's AI Adoption Surges in Daily Life, but Lags Behind in the Workplace," EY AI Sentiment Index, 15 April 2025. https://www.ey.com/en_uk/newsroom/2025/04/ey-ai-sentiment-index-2025
[8] EY 2025 Work Reimagined Survey, December 2025. https://www.ey.com/en_uk/newsroom/2025/12/uk-employers-miss-40-percentage-ai-gains-talent-gaps
