The Matthew Effect in AI: Why the Gap in Society Is Growing (and How We Close It)

AI promises equal opportunities for everyone, but reality shows the Matthew Effect: the gap between frontrunners and laggards is growing. How do we prevent a divided society?

The Illusion of the Great Equaliser

When generative AI like ChatGPT appeared on the scene, the promise was as utopian as it was clear: this would be the great equaliser. Everyone would suddenly be able to write like an experienced copywriter, plan like a strategist and learn like a top student. The technology would give people with fewer opportunities, less education or a smaller network a gigantic boost, making old differences disappear. It sounded wonderful. But the reality in society now paints a very different, much bleaker picture.

We are currently witnessing the so-called 'Matthew Effect' in AI adoption. This sociological principle, named after the parable from the biblical book of Matthew, roughly states: 'For whoever has, to him more will be given, and he will have abundance; but whoever does not have, even what he has will be taken away from him.' In other words: the rich get richer, and the poor get poorer. In the context of artificial intelligence, this means that people who are already doing well — well educated, digitally skilled, with a strong network — embrace the technology and do even better. Those who lag behind, conversely, are increasingly desperately avoiding the technology. This insight, recently sharply analysed by Job van den Berg in the science section of AI at Work Live (BusinessWise / DPG Media / New Business Radio), is now painfully and strongly substantiated by disconcerting figures from two recent, large-scale scientific publications.

The Danish Mirror: Existing Inequality on Steroids

Let's first look at the sociodemographic impact. In a broadly designed Danish study, titled 'The unequal adoption of ChatGPT exacerbates existing inequalities among workers', published by Anders Humlum and Emilie Vestergaard in the Proceedings of the National Academy of Sciences (PNAS, early 2025), it becomes painfully clear how skewed adoption really is. The researchers delved deep into the data to see who is actually using the tools, and more importantly: who is ignoring them.

The conclusions leave little to the imagination. It is primarily men, highly educated individuals, and younger people (though for the specific group of labour-market entrants, it is a bit more nuanced, as we will see) who are integrating ChatGPT and similar tools into their daily lives. What makes this research so crucial is the finding that this unequal adoption does not level the playing field, but rather puts existing differences in our society on steroids. Someone who is already highly educated, with a strong network and good skills, uses AI as a flywheel to get even further ahead: better cover letters, faster learning, smarter financial choices. Those who have historically been in a vulnerable position ignore the tool due to insecurity, disinterest, or simply a lack of accessible explanation.

Humlum and Vestergaard show that it is not the technology itself that creates inequality, but the human reaction to it. AI is seen by frontrunners as an exoskeleton that strengthens their cognitive muscles. However, those who use the technology least often perceive it as an elusive threat. Instead of experimenting, they hold on to familiar, less efficient habits. This is the Matthew Effect in its purest form.

The American Canary in the Coal Mine: Young People Are Missing Out

But it becomes even more concerning when we zoom in on the cold, hard data of the labour market. A paper from the prestigious Stanford Digital Economy Lab (November 2025), authored by heavyweights Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen, makes quite a splash. Under the title 'Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence' they looked at wage and employment data from millions of Americans via payroll processor ADP.

Their discovery includes a statistic that should keep all of us awake: young people aged 22-25, entering professions most exposed to AI, are currently experiencing a relative employment decline of no less than 16 per cent compared to sectors where AI has no influence. Sixteen per cent. That's not a small ripple; that's a structural shift in the opportunities an entire generation is given.

What do these figures explain? The researchers state that fewer entry-level positions are simply needed because experienced people, thanks to AI, can now handle the type of bulk and basic work themselves with the push of a button. Where a legion of fresh graduates was once needed to analyse data, draft texts, or write basic code, experienced hands now do this efficiently via a prompt. Young people, whom we often assume are 'digital natives' and would automatically reap the benefits of technology, turn out to be the absolute canaries in the coal mine here. They are on the sidelines before they have had a chance to prove themselves.

The Ironic Paradox of AI Adoption

Combining the Danish PNAS study and the Stanford ADP research, we arrive at the ironic paradox of this decade, which was also aptly highlighted by Job van den Berg. It is the ultimate irony of the current AI revolution: precisely the people who could benefit most from artificial intelligence are the ones who embrace the technology least.

Why is this? Why does the laggard avoid their life jacket? It has everything to do with fear of failure and a lack of a safe environment to learn. People who are insecure about their position — at work, in a side job, at school — are afraid to make mistakes with technologies they don't understand. 'If it goes wrong with ChatGPT, it's my fault. If I do it the way I always did, no one can blame me,' is the unwritten, internal dialogue. Those who already excel, on the other hand, possess the self-confidence to play, to fail in a prompt, to shrug off the output and improve it, and then to reap the rewards in time and quality.

What Can We Do as a Society: Not a Stick, But a Carrot

Here lies a gigantic, urgent task for all of us: for government, education, businesses and for everyone who has already mastered the technology. If we let the Matthew Effect run its course, in a few years we will end up with a polarised society. On the one hand, a small elite of super-productive, happy 'AI centaurs' (half human, half machine), and on the other hand, a growing group of frustrated people who are starting to feel redundant, or in the case of the 22-25 year olds, never even get to the start of their careers. That is not only bitter for those involved, it undermines the cohesion of society as a whole.

The classic reflex of institutions is often to use the stick: imposing obligations. "Everyone must use AI from now on," or, even worse: linking opportunities and provisions to digital skills people were never taught. This is exactly how you push insecure people further into the trenches. Coercion leads to superficial adoption: people pretend, but don't change their habits.

The advice, informed by insights gained through AI at Work Live, is clear: avoid the stick, use the carrot. Shift the focus from performance pressure to curiosity. Let people marvel. Here are four concrete, in-depth strategies to immediately reduce the gap in society:

1. Cultivate Wonder Without Judgment

Make room to play with AI, without anything depending on it. Think of libraries, community centres and schools where people can experiment without obligation, and of moments when the failed prompt is shared rather than the brilliant one. Make AI adoption a journey of discovery, not an exam. When the pressure of 'you have to be able to do this' disappears, there is room for genuine understanding of how such a model 'thinks'. The carrot in this case is the satisfaction of mastering something new.

2. Create Fearless 'Buddy Systems'

Don't pair the biggest AI enthusiast with the biggest technophobe as a kind of tutor. That creates shame. Put equals together — two neighbours, two parents from the same school, two colleagues with the same chore — and give them one task: "See if together you can make that one horrible chore easier with Copilot or ChatGPT." Learning together greatly lowers the barrier and creates pillars of support close to home.

3. Focus on the 'Job to be Done', Not the Technology

Stop explaining "How does a neural network work?" or sifting through prompt-engineering theory. Most people don't care about that. Speak the language of their pain. "Who always sighs at the tax return, the letter to the health insurer or writing a cover letter? Let's see if that can be done in five minutes instead of five hours." When you solve the pain points of everyday life, adoption happens naturally, and you bring the reluctant along too.

4. Re-evaluate Young People and Starters

Look at Brynjolfsson's alarming figure: the -16% for young people in exposed occupations. Don't close the door on an entire generation. If the bulk work that used to give them a chance is now done by AI, redefine the start of a career. Teach young people at school and in their first job to start directly as AI operators and quality controllers. They don't yet have the experience of older generations, so let them grow up together with the technology, so they learn to steer the AI instead of competing with it.

Time for a Society with a Human Touch

Technology is an ice-cold amplifier of what already exists. In a society full of friction and inequality, the introduction of generative AI will unforgivably widen that gap. Those who flourish will soar to new heights. And the laggards will simply wither away, sidelined by their refusal to surrender to a tool they do not trust.

We must move from abstract theory to warm practice. The Matthew Effect in AI can be defeated, but only if we as a society realise that the introduction of this technology is the greatest change management challenge of the past fifty years. And such a challenge is not solved with cold coercion or a software licence. It is solved with empathy, the space to make mistakes, and the nurturing of genuine, human wonder about a machine that will fundamentally change us all.

"Precisely the people who could benefit most from artificial intelligence are the ones who embrace the technology least."

Sources

• Humlum, A., & Vestergaard, E. (2024/2025). The unequal adoption of ChatGPT exacerbates existing inequalities among workers. PNAS.
• Brynjolfsson, E., Chandar, B., & Chen, R. (Nov 2025). Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence. Stanford Digital Economy Lab.
• Discussed insights partly based on a contribution by Job van den Berg in the science section AI at Work Live (BusinessWise / DPG Media / New Business Radio).

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