Imagine a seventeen-year-old choosing a course of study right now. She is weighing up law, something in administration, or a degree in design. She does what we have been advising her to do for years: pick a direction and invest seriously in an education, in money and in time. The adults around her quietly assume that by the time she graduates, an entry-level job will be waiting for her, and a career beyond it.

But that assumption is beginning to look increasingly shaky. The companies building the world’s most powerful AI systems have written down what their goal is. And that goal is not “AI as a handy assistant” — though that is often how they present it in media appearances. The goal is to take over the work itself.

OpenAI describes its mission as building Artificial General Intelligence (AGI): “highly autonomous systems that outperform humans at most economically valuable work.” Autonomous systems that surpass humans at most economically valuable work. That is the sentence with which one of the most influential companies of our time defines itself. It is worth letting this sink in. Outperforming human labour is the explicit mandate these companies have given themselves, and hundreds of billions of dollars are being raised to make it happen.

The sheer scale of investment in AI is itself an argument. The sums now flowing into data centres, chips and energy are so large that they are hard to justify with the story “we are building a handy assistant.” They only make sense if you assume that the real goal is not to speed up a task here and there, but to take over large portions of knowledge work from people. The business model of these companies depends on succeeding at exactly that.

GDPval — canary in the coal mine

A counterargument you often hear is: tech companies always exaggerate. That is true enough. But how can we assess what is actually happening?

OpenAI has developed a benchmark called GDPval that tests AI performance on real professional work. Think of drafting legal advice, building a financial model, writing a nursing care plan, producing a quote. The test consists of real assignments from 44 professions, completed by both an AI model and a human, then evaluated by experienced professionals who do not know whether they are looking at the human’s work or the model’s. If the AI’s output is judged to be better or equal to the human’s, that counts as a point for the AI.

When the study was conducted in 2024, the score of the best model rose from around 12 per cent to nearly 50 per cent in roughly a year — where a score of 50 per cent means that in half of the tasks the AI delivers work that is as good as or better than the human’s. By late 2025, a newer model was already at around seventy per cent equal or better. This concerns knowledge work, not physical work, and a draw counts in AI’s favour. But the direction is clear, and so is the speed.

In other words: for a growing share of well-defined knowledge tasks, an AI model now produces output that an experienced professional can no longer distinguish from human work. That does not mean entire jobs are disappearing yet. But jobs are made up of tasks, and the entry-level roles in which young people learn a trade often consist of precisely those kinds of tasks.

Are we seeing a wave of mass layoffs due to AI? No, not yet. But that is also not what you would expect: labour markets are notoriously slow to respond to new technology, because introducing new systems takes years. The computer, for example, took decades to really make its way onto the shop floor. So there is no reason for panic — but there is every reason to pay close attention and take preparatory steps now.

The difficulty is that we are poor at imagining a world that is fundamentally different from the current one. We unconsciously project today’s labour market into the future, just with an AI veneer on top. The possibility that the structure of the labour market itself might change — that the relationship between work and income might look different — falls outside our frame. And what we cannot imagine, we cannot prepare for. We need to stretch our imagination.

Two futures, both achievable

The gloomy picture is easy to sketch. A labour market where the bottom rungs of the ladder have been removed, where young people find no foothold to learn a trade, where the gains from automation flow to a small group and the costs fall on everyone else. A society wealthier than ever, yet declaring whole groups of people redundant. That is the outcome you get if you let the current incentives run their course and do nothing more.

But there is another outcome, and it is equally achievable. If machines take over a large share of necessary work, something is freed up that has historically been scarce: time and human attention. Time for care, for education, for each other. For restoring nature, for work that currently goes undone because it is not profitable. A society that decouples prosperity less rigidly from paid employment, and gives people the space to contribute in ways the market poorly rewards.

Three measures

Because nobody knows how AI will develop or how quickly, it makes sense to start with measures that are worthwhile whatever the future looks like. Here are three.

The first: a solid safety net. We already have systems — like unemployment insurance — that work as automatic stabilisers the moment people lose their jobs, regardless of the cause. As the economist Martha Gimbel puts it: don’t get too creative with your solutions for an AI-disrupted labour market. Strengthen the systems that already exist, because they are flexible, they work automatically, and they have proved their worth in previous technological transitions. Whether the shock turns out to be large or small, a stronger safety net is never wasted investment.

The second: measure what matters. We cannot respond to displacement we do not see coming, so we need a system that makes shifts in the labour market visible before they become a crisis. Invest in reliable, detailed, and timely information.

The third: start thinking now about preparing an alternative fiscal and social architecture, so that we do not have to improvise in a panic if change arrives suddenly. There is plenty of input for this — more on that in a future post.

None of these steps requires you to believe that all work will vanish next year. But they are useful first steps if you take the possibility of an AI-seriously-disrupted labour market seriously enough to prepare for it.

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