After Jesus’s resurrection, the apostle Thomas refused to believe it had happened until he could see and touch the wounds for himself. When he finally did, he believed — hence the expression “je ne crois que ce que je vois” — I only believe what I see.
Thomas has given his name to generations of skeptics. But he wasn’t alone.
Not long ago, many people struggled with the idea of the unseen. When asked, “What colour are the bears at the North Pole?” even after being told that bears in snowy places are white, and that the North Pole is nothing but snow, they would answer: brown. Why? Because brown bears were all they had ever seen.
The problem wasn’t ignorance. It was something deeper: a limit in how people processed information. Our ancestors weren’t used to hypothetical thinking. Their minds were trained by the world around them: immediate, concrete, visible.
Between 1900 and 2000, IQ scores rose sharply across the globe, especially in the domains of abstract reasoning and problem-solving. Psychologists call it the Flynn Effect. The rise wasn’t due to better genes. It was driven by new expectations – more schooling, more jobs requiring mental flexibility, and a society where asking “what if?” became a form of survival.
At the dawn of the 19th century, America was still an agricultural nation. Over three-quarters of its workforce lived on farms. The story was not so different in Europe or the rest of the world, where the economy ran on fields, factories, and fixed routines.
But in the last 50 years, everything changed. Manufacturing peaked and declined. The service sector exploded. Freelancing emerged. Remote work became a reality. Today, nearly half of all jobs require some form of cognitive, creative, or interpersonal skill. We no longer work just with our hands – we work with our heads.
And that was before AI.
For decades, the ideal was clear: educate yourself, specialise, get a degree, get a job.
And it worked – for a while. Global access to education has expanded dramatically. Across many countries, the share of the population holding university degrees has never been higher. In some markets, entire generations are now overqualified for the roles they occupy.
Take Norway, for example – a country with one of the highest university degree attainment rates in the OECD. Yet unemployment among graduates is rising, revealing a growing mismatch between formal education and market demand. And this isn’t just about Norway — it’s a preview of what happens when an overqualified workforce meets an economy shifting faster than its institutions.
The job market is quietly shifting. PhDs drive taxis, while master’s graduates fill spreadsheets that AI may soon automate entirely.
At the same time, early signs of deeper disruption are beginning to surface — particularly for those just entering the workforce. A 2025 study by Hosseini and Lichtinger highlights a noteworthy trend: since the release of GPT‑3.5, firms adopting generative AI have reduced their hiring of junior employees, even as they continue to grow their senior ranks.
It’s a break from the traditional model, where junior talent fueled growth and climbed the ladder over time. In contrast, firms not using AI have continued to hire younger professionals at a stable pace.
This divergence is telling. But it’s also early data. While AI may be reshaping the bottom of the career pyramid, it’s still unclear what new entry points, if any, will emerge in response. What’s clear is that many of the tasks that once justified junior roles – drafting, researching, summarising, coordinating – can now be done faster, cheaper, and often better by machines that never tire.
And this time, automation isn’t coming for factory floors.
It’s coming for emails, presentations, reports, and all the foundational tasks that once served as a training ground for the next generation of leaders. The traditional apprenticeship model – learn by doing, rise through experience – is being quietly rewritten.
This, however, is just the tip of the iceberg. We are only at the very beginning of automation powered by large language models and machine reasoning.
We’re not immune to these changes.
It once took years to convince people to wear watches on their wrists instead of in their pockets. Now, we’re facing an entirely new kind of challenge – a battle not just for attention, but for the wrist itself.
Today, mechanical watches compete with the most valuable company in the world: Apple, which has already sold over 200 million smartwatches. These devices offer not just the time, but health tracking, payments, messaging, and even AI-powered coaching, all wrapped around your wrist.
“Yes, but what we do is different.”
True. Yet, that frame misses the point.
Think of the car industry. Classic cars still exist – they’re admired, collected, and celebrated. But they’ve vanished from everyday life. Not because people stopped loving them, but because incentives changed. Fuel taxes rose. Maintenance costs climbed. EVs became faster, cheaper, and more practical. In the end, the logic of progress reshaped desire.
Now imagine a world where your smartwatch isn’t just a gadget, but your health passport. Your insurance premium drops if you wear it. You get access to benefits, programs, early diagnoses, and even AI-generated longevity plans – all seamlessly integrated. If that future arrives (and it’s already here in parts), what happens to Switzerland’s share of the wrist?
This is not the end of mechanical watches. But it may be the end of complacency.
In the age of AI, our task is not to resist the future – but to redefine relevance. We must ensure that mechanical watches aren’t just admired, but chosen – not for utility, but for what they say about the person wearing them: their values, their sense of permanence, their understanding of time as something deeper than convenience.
Because in a world moving faster than ever, the most human thing we can do may be to slow down – and wind that crown.
After all, as we’ve seen from history, not everything must be seen to be believed.
This article originally appeared in Europa Star.
