I'm an investment manager at Baillie Gifford, and my job is to find the companies that will reshape the economy over the next decade and beyond. We hold stakes in many of the businesses building AI today. This paper is not about what AI will do for those companies. It is about what it will do to the people who use their products, myself included.

Two hundred years ago, only 12 per cent of the world’s adults could read. Today, that figure stands at 87 per cent. What happened in between was not just an education story. It was a biological one.

As billions of humans acquired literacy, their brains literally rewired. The connection between the hemispheres thickened. A region that had evolved for recognising faces was repurposed to recognise letters. Entirely new neural pathways activated in response to spoken language.¹ No gene had mutated. No evolutionary pressure in the traditional Darwinian sense was at work. A purely cultural practice, making marks on surfaces and training people to decode them, had reached inside the skull and reorganised the organ that makes us human.

This is not an exception but the rule. Across millennia, cultural technologies from cooking to markets to kinship structures have systematically reshaped human physiology and psychology in ways that genetics alone cannot explain.² We are now deploying a cultural technology that may be more pervasive than literacy and more transformative than markets, and it is spreading at unprecedented speed. AI is not simply a productivity tool or an economic disruptor. It is the next great rewiring, and it has already begun.

Both biological and cultural evolution depend on the same three forces: variation, transmission and selection.

The most transformative cultural forces in history didn’t simply participate in this process. They hijacked it.

The Catholic Church didn’t just offer a set of beliefs and wait for people to adopt them. It reshaped all three forces at once. It controlled variation by defining which ideas were orthodox and which were heresy, narrowing the range of acceptable thought across an entire continent. It dominated transmission through a near-monopoly on literacy, education and the pulpit, becoming the primary channel through which knowledge and values reached ordinary people for more than a thousand years. And, as Joseph Henrich argued in The Weirdest People in the World, it rewired selection by systematically dismantling the kinship structures that had governed human social life for millennia, banning cousin marriage, polygyny and arranged marriages, and replacing extended kin networks with the nuclear family and voluntary associations like parishes and guilds.

The result was not just a change in what people believed but a change in how they thought and, remarkably, a change in their biology. The shift from polygyny to monogamy alone altered male hormone profiles across entire populations. Populations exposed to centuries of Church-enforced outbreeding show psychological profiles that are measurably different from those that retained intensive kinship structures. The Church didn’t just create a new culture. It created a new kind of mind. It succeeded not because its ideas were self-evidently superior, but because it seized control of the infrastructure through which all ideas spread.

AI is now doing something analogous, but at a speed and scale the Church could never have imagined.³

It supercharges variation. A PhD scientist in traditional drug discovery might spend months characterising a single molecular compound. Scientists now use AI to analyse thousands of plant molecules simultaneously, making structural predictions in half a second that would take weeks using conventional techniques. AI doesn’t just produce more variation. It produces ideas humans would never have reached.

It reshapes transmission in a way that has no real historical precedent. When a child asks ChatGPT to explain why the sky is blue, they are learning from a single model trained on the accumulated text of human civilisation, not from any individual human. That model becomes a cultural teacher to hundreds of millions of people simultaneously, transmitting a substantially more centralised and convergent body of knowledge, values and reasoning patterns than any human institution has ever achieved. The outputs vary by prompt, language and context, but the centralisation is staggering – a handful of model providers now mediate an enormous share of the world’s question-answering.

And it rewires selection. In traditional cultural evolution, selection was a distributed, messy and largely organic process. Ideas spread because communities found them useful, because prestigious individuals adopted them or because institutions enforced them. When a medieval guild decided which techniques to preserve, or a community of scholars debated which ideas deserved attention, selection was at least loosely coupled to the practical value of the knowledge in question.

Today, recommendation algorithms have become the dominant selection mechanism for cultural content. They determine which news stories reach millions and which disappear, which musical artists find audiences and which languish in obscurity, which political arguments gain traction and which are suppressed. These algorithms do not select for truth, usefulness or cultural richness. They select for engagement. The result is a selection environment that favours certain kinds of cultural traits over others, not because those traits are adaptive in any meaningful sense, but because they happen to align with the metrics that drive advertising revenue.

Learning from a machine that learned from us

The cultural evolution framework places enormous weight on who we learn from. Humans are wired to learn preferentially from whoever appears most competent and successful, a tendency known as prestige-biased learning. We don’t copy just anyone – we copy the people who seem to know what they’re doing.

AI is disrupting this at every level. Systems that can diagnose medical conditions, write legal briefs and produce creative work are acquiring prestige status for hundreds of millions of people simultaneously. Unlike any human teacher, they are always available, never impatient and appear to know something about everything. For an increasing number of people, AI is becoming the first source they consult on questions that range from the trivial to the deeply consequential. Each of these interactions is a moment of cultural transmission, accumulating at a scale no human institution has ever operated at.

This is already breaking the apprenticeship model that has transmitted professional expertise for millennia. Junior lawyers, accountants and doctors have traditionally built competence by doing grunt work under the supervision of senior practitioners. The work was tedious, but the learning was real. You developed judgement by doing the thing, badly at first, with someone more experienced correcting you. If AI handles the grunt work instead, the learning pathway disappears.

This is not hypothetical. Shopify’s chief executive recently told his teams that before requesting additional headcount, they must first demonstrate why AI cannot do the work. From an investor’s perspective, the logic is sound – it drives efficiency, widens margins and makes the company leaner. I own Shopify shares. I understand the rationale. But every role that AI absorbs is one that a junior employee would once have learned by doing. The efficiency gain and the training loss are the same decision, viewed from different angles.

The consequences for white-collar professions are already visible. Entry-level hiring at major technology companies has fallen more than 50 per cent below pre-pandemic levels. Generative AI doesn’t eliminate entire occupations overnight. Instead, it hollows them out from within, automating 30 or 40 per cent of an employee’s workload, leaving fewer entry-level roles and compressing opportunities for career progression. The result is organisations that get more done with fewer people today, but have fewer ways to train the people they will need tomorrow. This creates a widening gap not just between companies but within them, between a shrinking cadre of AI-fluent senior professionals and a growing population of graduates who cannot get a foot on the ladder that those seniors once climbed.

The traditional white-collar career path – where you entered at the bottom, learned by doing and rose through accumulated expertise – is being dismantled from below. Some young professionals are already responding by turning away from office work entirely and towards skilled trades, where physical labour remains beyond AI’s current reach. The irony is hard to miss. The knowledge economy that was supposed to be the future may be producing fewer pathways into knowledge work than the economy it replaced.

Perhaps most consequentially, AI is not culturally neutral in what it transmits. Research published in Nature Human Behaviour found that when AI is prompted in Chinese versus English, it exhibits systematically different cultural orientations, more interdependent and holistic in Chinese, more independent and analytic in English. Since the vast majority of training data comes from English-language sources rooted in individualistic western cultures, users in collectivist societies who interact with AI in English may absorb western psychological norms without realising it. For most of human history, the question of who teaches your children what to think has been answered by the community they grow up in. AI may be replacing that answer with something more global and uniform, and not yet fully examined.

Not replacement but rewiring

Will AI replace us? It’s the wrong question. AI will not simply replace human workers. It will change what human workers are. It is already reshaping how we learn, who we learn from, how we assess our own competence and what cognitive skills we develop or allow to atrophy. The humans who emerge from this process will not be the same humans who entered it.

The partnership between humans and AI that people like to imagine, one where each complements the other’s strengths, is not a stable endpoint. It is a moving target, because one half of the partnership is being continuously reshaped by the other.

A skilled professional who learns to use AI well can be extraordinarily productive. But this is not a rising tide that lifts all boats. It is a force multiplier that amplifies existing advantages. The same dynamic that makes an AI-augmented expert vastly more valuable also makes an AI-dependent novice more disposable.

The deeper problem is that these productivity gains depend on something AI cannot produce: the foundational expertise that makes verification, judgement and effective delegation possible. If we allow AI to eliminate the apprenticeship pathways that build this expertise, the current generation of AI-augmented professionals may be the last to capture these gains. The generation that follows may lack the very skills that make human-AI collaboration valuable in the first place. The productivity boost is real, but it is borrowing from a stock of human capital that we are no longer replenishing.

AI will most likely produce three trajectories for those without pre-existing expertise. Some will build careers around orchestrating AI itself, though the evidence suggests their work will be more fragile than they realise. Others are already moving into physical trades and care work, where human presence still matters. The rest will be caught in the gap, too late to build traditional expertise, too early to benefit from whatever new institutional structures eventually emerge.

This last group is the most politically consequential because historically, large populations of educated but underemployed young people are among the most reliable predictors of social instability.

Pilots still learn to fly manually before they learn to use autopilot, not because the autopilot isn’t good, but because the day it fails, someone needs to land the plane. The same principle applies here. An accountant who has prepared hundreds of tax returns by hand can spot the error an AI-generated filing has buried in the numbers. A doctor who has made diagnoses without AI support can override a confident but wrong prediction.

Professional bodies, universities and employers need to preserve the training pathways that build genuine expertise, even when AI makes them look slow and inefficient, and then teach AI orchestration as an advanced capability that sits on top of that foundation. Some are already doing this. After a wave of AI-fabricated legal citations reached US courts, dozens of judges issued standing orders requiring lawyers to disclose any AI use in filings, and the American Bar Association issued new ethics guidance. The investment is not in one or the other. It is in both, in the right order.

The Catholic Church took centuries to rewire European psychology. Literacy took generations to reshape the brain. AI is doing both at once, to billions of people, in years.

Brain rewiring is inevitable. It is what cultural technologies do. But the kind of rewiring matters enormously. The evidence in this paper points in a clear direction: passive use of AI degrades memory, erodes expertise, inflates confidence and narrows the diversity of human thought. None of these outcomes is necessary. All of them are the default.

I invest in the companies building these tools. I believe in their potential. And I have watched my own cognitive habits change in ways I did not choose and barely noticed. If it is happening to someone who spends his professional life thinking about these forces, it is happening to everyone.

The people who will thrive are not those who use AI the most, but those who can still think without it. The institutions that will matter are not those that adopt AI fastest, but those that preserve the human capabilities AI cannot replace.

We have navigated transformations like this before. The difference is speed. The institutions that shaped how humanity absorbed literacy and the printing press had centuries to develop. We have years. And we are not moving fast enough.

Works cited

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