Future of work, reworked
AI is disrupting the economy as we know it. We have a duty to contribute to AI transformation as co-authors and not just subjects.
Picture a Tuesday in 2036: you log on to find the night’s work already done – your four agents have drafted, negotiated and reconciled amongst themselves, leaving you a short list of judgement calls no machine is licensed to make. Your performance review measures not output but discernment: the quality of your overrides. A colleague’s bundle came apart last quarter; she now sells the one task that wouldn’t separate – being accountable. The office, when you visit it, is a place for friction of the deliberate, constructive kind, since agreement has become abundant and cheap. Nobody asks what you do anymore; they ask what you decide.
This future may seem far-fetched or too close to the bone, depending where you find yourself. Whatever job you have or are about to enter, you are likely considering the impact of AI disruption on how you work. You know the future of work is to be reworked by AI, but you may be questioning what is within your power to steer its reworking and where can you pick up the pen? Jobs come across as a full package, but how can we define them in finer nuance to unpick where the human and the machine fit in this emerging productivity package and intricate intelligence interaction design?
Untangling job displacement
12 months ago, the Anthropic CEO famously claimed 50% of all entry-level white-collar jobs may be eliminated within the next 5 years1, though that claim was walked back recently as the data indicates automation actually expands the work of current professionals2. Today, in June 2026, we see 49% of jobs where at least one quarter of tasks are being performed with Claude3. If the headlines might seem scaremongering as AI systems are expected to automate or replace entire jobs, once you look more carefully, the current sentiment leans more towards human-AI collaboration or augmentation whereby we each leverage our unique feats and capabilities.
AI adoption is high at the level of the individual worker, with the often-quoted figure of 95% of professionals using shadow AI – that is, tools not endorsed or provided by their employer – but still low at organisation level, with only 1 in 6 organisations using AI in at least one business function across the global North spectrum - the UK4, US5 and EU6. The gap matters: it means the transformation is being driven bottom-up, worker by worker, ahead of any procurement, policy or governance decision.
Before going further, it helps to separate three things the headlines tend to blur. Exposure is what AI could technically do – the share of a job’s skills that systems are already capable of performing7. Adoption is what is actually being used – the territory the UK, US and EU surveys map4,5,6 and the ground Anthropic’s Economic Index measures conversation by conversation3. Displacement is what happens to people – the jobs restructured, the hiring frozen, the careers rerouted. The three are routinely conflated, and almost every dooming argument about the future of work begins by mistaking one for another. The sections that follow try to keep them distinct: first the instruments for measuring exposure, then the displacement as it is felt, followed by imagining how we can steer our future of work unfolding.
From exposure to adoption
The general rhetoric presents jobs as the source of livelihoods, and they come as a package which are either available or are not. In an effort to untangle the concept, MIT’s recently launched Project Iceberg7 proposes a novel skills-oriented framework which calculates the proportion of economic wage value attributed to competencies that AI systems are technically capable of executing in a given role. The index was constructed by modelling 151 million agentic human workers as AI agents across 923 professions and some 32,000 skills, evaluated against the capabilities of thousands of AI tools via high-performance computing. It specifically gauges technical exposure prior to actual adoption, and is explicitly not a direct measurement of workforce displacement.
The insights are sobering. Visible AI adoption, concentrated in computing and technology, accounts for around 2.2% of wage value; yet the skills AI systems can already technically perform extend to 11.7%, roughly $1.2 trillion in wages, submerged across administrative, financial and professional services. This indicates exposure to AI is in fact five-fold larger than what we see adopted already, and that doesn’t account for ongoing and future AI developments. The framework is validated by a skill-based occupation similarity predicting 85% of real career transitions, illustrating skills are a meaningful measure of human-AI work interaction. As the industrial era measured output per hour, and the internet era built the digital economy accounts, the intelligence era requires a new metric – one which measures skills. We are perhaps flying blind because our instruments measure the wrong thing.
In a similar vein, others have proposed that jobs are bundles of tasks8, criticising how labour markets buy jobs, not tasks, and asking whether AI can separate a task it performs well and cheaply from the larger bundle. When separation is cheap, the bundle is weak and the human role narrows. When separation is expensive, the bundle is strong and AI raises productivity inside the bundle without replacing it. To make the abstraction concrete: a paralegal, whose document review separates cheaply from the rest of the role, sits in a weak bundle and watches the role narrow; whereas a nurse, whose tasks are bound together by physical presence, unpredictable demand and strict liability, sits in a strong bundle and watches AI raise her productivity without replacing her. The common patterns that make a bundle strong follow from the example: demand is unpredictable, so the cost of switching between human and AI is too high; production spillovers mean doing one task makes one better at the next; and liability requirements are strict, so human-AI joint outputs must be attributable to a human.
The more AI commodifies what used to be considered ‘non-routine’ cognitive tasks, like crunching numbers and generating text, the more the human value proposition will necessarily shift toward capabilities that no machine can (yet) reliably replicate: applying judgment in messy, evolving contexts; connecting disparate ideas into a coherent plan; and persuading, negotiating and leading when the ground is shifting underfoot.
Everyone is exposed to AI
That doesn’t mean we will all adopt AI in our jobs or even more so, be at risk of displacement. Painting a richer picture is nonetheless helpful. For the past few years, we have feared AI disruption will affect first and foremost entry level jobs, as those require most effort in training and mentorship and this slice of the economy can be first slashed as it isn’t even embedded yet in the workforce. In 2026, we see this unfolding in real time: in the UK, an independent report warned of a lost generation, with 1 in 8 young people aged between 16 and 24 not in education, employment or training9, with task and job automation cited as a contributing factor. In alleviating AI displacement, we are already seeing initiatives sprouting up to support entry-level workers navigating the transition to an AI-powered workforce, such as the New Work Foundation non-profit10, funded by a former Meta executive astounded at the AI agent capabilities she observed in real time.
Middle management jobs are not protected from the advent of AI either, with 1 in 5 organisations predicted to cut half of their middle management in 202611, in an effort to cut down labour costs, streamline reporting and business processes, and ultimately help flatten out the workforce hierarchy. For the individual manager, the flattening means the rung they were climbing towards may simply not exist by the time they reach for it. Senior leadership is challenged too in the AI era12, as experience alone and authority are not sufficient to efficiently lead companies towards becoming AI mature, and this group needs to embody other values too, such as empathy, critical thinking and ethical decision-making. When working alongside intelligent systems and autonomous AI, traditional hierarchies prove both redundant and obstructive, with adaptability and proportional risk-taking superseding principles rooted in domain expertise and upwards career progression.
When looking at the type of jobs affected, recent reports indicate both white and blue collar jobs are heavily affected, with IMF forecasts indicating AI is or will impact 40% of all jobs worldwide13. If real-world environments were a challenge for these disembodied AI systems, claimed by some as a key limitation of LLMs, we are now witnessing a shift towards embodied, perceptive and physical AI systems, as exemplified clearly by Yann LeCun’s Advanced Machine Intelligence Labs, which raised over $1 billion in a matter of months14. This is to say most sectors and job types are or will suffer changes, even those which might seem protected, such as care and nursing.
Anecdotally, frontier AI labs have recently been recruiting academic philosophers, leveraging on their deep enquiry methods15 – humanities backgrounds may bring growing value to an AI-fuelled economy: quantifiable skills are first to automate, leaving critical inquiry and risk-based assessment amongst the most valuable traits we can bring forth in our jobs. The same force points the other way in the sciences, where AI is compressing timelines for discovery and validation from decades to months16, holding promise to accelerate societal progress on the biggest challenges, such as disease and longevity. Commodified skill and accelerated discovery are two faces of the same transition.
A different Tuesday
The year is 2046. You wake up at the sound of notifications and powering engines, the AI system has allocated you a series of instructions for the day, carrying tasks you didn’t write to people who didn’t ask. Overnight the system has decided; your work is to give its decisions a human face, as a voice still lands softer than a screen. Your performance review measures compliance latency – how fast an order travels from the model to the floor. A colleague paused last quarter to question a number, and was quietly reassigned; the rest of us learned not to. The office, when you’re called in, exists for the appearance of consent – meetings where the conclusion arrives before the conversation. Nobody asks what you think anymore; they ask whether you passed it on in time.
Indeed, this is the dystopian end of the spectrum where we could be experiencing a fast and fundamentally transformative shake-up of the economy and society where humans execute and machines instruct. The AI transformation might not only challenge our sense of purpose and meaning as individuals and civilians alike, but it may leave us questioning whether we are the most superior intelligent entities in an evolving landscape where artificial supersedes biological. Bringing together minds and behaviours in a collective intelligence super-engine which powers all employment sectors might leave humans in an uncomfortable yet comforting position – we cannot hold any more subject matter expertise, but we can learn how to best engage with the same knowledge available to us all. Negotiation skills with human stakeholders transform into humans managing conflict resolution between AI agents.
Revert back from 2046 to 2036
We cannot pause or stop AI outright; too much capital is staked on its continuation, and as a general-purpose technology it is penetrating nearly every job sector and human task as illustrated above. There are, however, brakes on the machine – the EU AI Act, compute governance, liability regimes – and the honest claim is not that they can halt the transition, but that they can shape its speed and distribution. We can slow adoption where harm outpaces understanding, cut down the hype, and tone down the overpromise of solving all societal grand challenges or surpassing humans in all cognitive work tasks. We might move from economic productivity and the mechanical optimisation of a corporation’s share price to evaluating how work impacts wellbeing, keeping a tight balance between task automation and human augmentation. We could build together a vision more grounded in the messy human reality we live in, and put the human at the centre of this revolutionary transition, so that we maintain our anthropocentric status quo and reclaim our humanity.
I lay below five commitments, for the transition from a dystopian to a human-centered, machine-aided future:
We will become AI literate at the speed of humans, not the speed of frontier model releases – accounting for the asymmetry between how fast models ship and how fast people learn, and for the incentives at play when AI is embedded within all strata of society.
We will demand AI is designed with a human-centred focus – from user experience to expressing uncertainty and exercising metacognition – that encourages critical judgement and human skill improvement over cognitive decay.
We will hold providers and deployers, policy makers and organisation leaders to adaptability and empathy, so that human jobs are retained, transformed or created whilst reaping the imminent benefits of intelligent systems.
In a world at risk of sycophancy and convergence of thinking, we will value friction and diversity in all their shapes and formats.
And whilst the power does not lie with the individual, collectively we can steer the rhetoric – meeting the job market transformation with the information and tools to treat it as what it is: both a threat and an opportunity, in proportions that depend on what we do.
An open invitation
Today, many of us may feel like subjects of AI displacement; tomorrow, we could be authors and co-creators empowered with the knowledge and competencies to engage critically and constructively in a discussion which involves us all – when work as we know it transforms fundamentally, who do we become, and what is our role in the becoming process? Scientists, economists and policy makers build forecasts; humanists and writers imagine scenarios, fictionalise them and turn metrics into maps. When they become believable, we can choose to inhabit that reality, and – one action and reflection at a time – we build intricate causation chains. AI displacement is a matter of both now and the future, and it is both a concern and an opportunity.
If you believe this is a worthwhile discussion, please join us in Berlin on 2nd of July to co-create fiction scenarios and nourish hope.
References
Anthropic CEO says AI could wipe out half of all entry-level white-collar jobs — Business Insider, 2025.
Sam Altman and Dario Amodei are both walking back their AI jobs apocalypse prophecies as they eye blockbuster IPOs — Fortune, 2026.
Anthropic Economic Index report: Learning curves — Anthropic, 2026.
AI Adoption Research — UK Department for Science, Innovation and Technology (gov.uk), 2026.
Monitoring AI Adoption in the U.S. Economy — Federal Reserve, FEDS Notes, 2026.
Use of artificial intelligence in enterprises — Eurostat, Statistics Explained, 2025.
The Iceberg Index (Project Iceberg report) — MIT (iceberg.mit.edu), 2025.
The task is not the job — Silicon Continent (Substack), 2026.
Young People and Work: Interim Report — UK Government (gov.uk), 2026.
Meta / Salesforce executive launches non-profit to help Gen Z navigate AI agents and jobs — Fortune, 2026.
Why Companies Cutting Middle Managers To Fund AI Is A Mistake — Forbes, 2026.
Why AI Demands a New Breed of Leaders — MIT Sloan Management Review, 2025.
AI Job Replacement Statistics — DemandSage, 2026.
Yann LeCun Got $1 Billion For World Model AI: These Robots Learned 1,000 Real-World Tasks In 24 Hours — Forbes, 2026.
Meet the One Woman Anthropic Trusts to Teach AI Morals — The Wall Street Journal (WSJ Magazine), 2026.
Accelerating scientific discovery with AI Co-Scientist — Nature, 2026