The Care Dividend: A Policy Framework for Turning AI Productivity into Social Infrastructure
AI is widening inequality, not closing it. Jamie Bykov-Brett argues for taxing concentrated AI profits to fund the care economy that holds everything up.
Abstract
In April 2026, Reese Witherspoon told women that using generative AI is feminist & that they need to "catch up" before their careers are overtaken. That framing misses the point & in practice makes things worse. Across IMF, OECD, ILO data & a 4,000-worker Financial Times survey, the pattern is blunt: AI uptake is widening existing income & gender gaps, not closing them. Meanwhile, unpaid care work, overwhelmingly performed by women, is doing trillions in economic heavy lifting & still gets treated like it's worth nothing. Instead of telling women to personally upskill their way out of structural problems, I argue for a Care Dividend: taxing a share of concentrated AI-linked profits & redirecting that revenue into the care economy. I use the UK as a live test: run a few levy scenarios against the government's upper-bound estimate of £47 billion in annual AI gains, then check what those sums would actually buy in care reform, from care worker pay to Carer's Allowance, childcare, & pension credits. No, AI tax won't pay for every hour of unpaid care. It can still fund a serious, recurring reform package. For me, the feminist response to AI isn't "learn faster." It's making AI-era profits pay into the care work already keeping society running.
Introduction: The Same Gap, Eleven Years On
In 2015, I stood on a TEDx stage in Wandsworth & made an argument that felt, at the time, like it should have been obvious. Digital inequality, I said, stems from the fact that our economic system hasn't kept up with our technological capabilities. In practice, tech usually amplifies the inequalities already baked into the system. Telling people to just "learn faster" isn't enough. We need systems designed around the social cost of change.
I'd spent years in youth work by that point, supporting young people through the Prince's Trust who had been hit hardest by digital exclusion. Young people with poor qualifications, thin networks, & basically no safety net. I watched them try to break into a job market that was already automating the entry-level roles they were aiming for. The people with the fewest resources were being asked to compete in a race that was rigged before they reached the starting line. I saw the economic hit, yes, but also the damage to confidence & identity. In the 2015 Prince's Trust Youth Index, 36% of young people said they felt inadequate on a daily basis. That figure leaped to 70% for those not in employment, education, or training for more than six months. When the economy locks you out, it doesn't just cost you a wage. It costs you your sense of place in the world.
The Prince's Trust has since become the King's Trust. The index has now been running for over sixteen years. & the picture has not improved. The King's Trust TK Maxx Youth Index 2025, surveying 4,285 young people aged 16 to 25, found that emotional health scored just 62 out of 100, recovering marginally from 60 the previous year but still near record lows. Financial wellbeing scored 58, only two points above the lowest level ever recorded & below the floor set during the 2008 financial crisis. Among the roughly 946,000 young people not in education, employment, or training, 50% said they felt hopeless about their future, 53% said the longer they were unemployed the worse they felt about themselves, & 44% believed they would fail in life (King's Trust, 2025). For context, that last figure was 34% in 2022. It has risen by ten percentage points in three years. Those 2015 figures weren't a blip; they were an early warning we mostly ignored. The trajectory since then has been downward across nearly every measure, with the 2022 & 2023 indices flatlined at all-time lows.
Eleven years later, the technology has changed but the pattern hasn't. Now the gap is wider, the stakes are higher, & we're still hearing the same thin advice in updated language.
In April 2026, Reese Witherspoon told Fast Company that using generative AI is feminist & that women risk having their careers "taken over" if they don't learn the technology. The backlash was immediate, but most of it focused on the wrong thing. She's right that AI matters. Where it falls down is treating a structural issue like a personal productivity challenge, which is the exact mistake I was pushing back against in 2015.
"Catch up" only works if the race is fair, & it plainly isn't.
A Financial Times-commissioned survey of 4,000 workers in the UK & US found that more than 60% of high earners use AI daily, compared to just 16% of lower-paid workers (FT, 2026). Men are more likely than women to adopt AI across nearly every sector, including tech, education, & retail. Usage is highest among lawyers, accountants, & software developers. So far, AI isn't levelling the field; it's mostly accelerating people already ahead.
None of this is new, to be honest. Every major technological shift, from the printing press to the industrial revolution to the internet, has played out roughly the same way. People with capital, skills & access capture most of the upside; people without those buffers carry most of the downside. What's different this round is how fast it's moving & how wide it's landing. AI is faster than anything that came before it, & it targets cognitive work, not just manual labour, which means the middle of the labour market is exposed in ways it wasn't during previous waves of automation.
In my TEDx talk, I described the job market in the information age as an eye shape: the middle-class paying jobs being automated, squeezed into lower-paid service roles that are harder to automate. Customer-facing, care-facing, human-facing work. That observation has only become sharper. The ILO now reports that female-dominated occupations are almost twice as likely to be exposed to generative AI as male-dominated ones, 29% versus 16% (ILO, 2026). The roles disappearing are disproportionately the ones women hold. The roles remaining are the ones that require human connection, human judgement, human presence. Care work sits at the centre of that.
So I'm arguing for a different feminist response to AI. Less "move faster," more "make the gains pay into care." Specifically, a Care Dividend: taxing a share of concentrated AI-linked profits & directing that revenue into the care economy. I use the UK to pressure-test what this could fund, what it can't, & where the design choices matter.
The Evidence: AI Is Not an Equaliser
Look at who is actually using these tools. A Financial Times-commissioned survey of 4,000 workers in the UK & US found that over 60% of high earners use AI every day. Among lower-paid workers, the figure was 16% (FT, 2026). That gap has nothing to do with curiosity or willingness. It has everything to do with the kind of job you have, whether your employer is investing in AI, & whether you have the bandwidth to experiment with new technology when you're already stretched across two jobs to cover rent.
Now layer gender on top of that. The OECD published an analysis called "Algorithm & Eve" which found that men are more likely than women to use AI at work across nearly every sector. Women face a compounding problem: less access to the tools, less likely to work in sectors investing in AI, but more likely to be in occupations exposed to automation without the productivity upside that comes with AI augmentation (OECD, 2024). In plain terms, women are more likely to be replaced by AI & less likely to benefit from it.
The ILO's numbers make this concrete. Female-dominated occupations have a 29% exposure rate to generative AI. For male-dominated occupations, it's 16% (ILO, 2026). That doesn't automatically mean those jobs disappear. But the pattern so far is that augmentation, the good kind of AI exposure, happens in higher-paid roles. Displacement, the bad kind, is concentrated in lower-paid ones. I've watched this play out in organisations I've consulted for. The senior team gets Copilot licences & productivity boosts. The admin team gets made redundant.
The IMF's own analysis is blunt about where this leads. AI will affect roughly 40% of jobs globally, more in advanced economies. It complements high-income workers & substitutes for lower-income ones. Without deliberate policy intervention, the result is wider inequality, not narrower (IMF, 2024). A separate IMF working paper on rapid AI adoption found the same thing from a macroeconomic angle: fast diffusion could boost output while concentrating gains among capital owners & the already highly skilled.
We're still early. Only one in five UK firms currently use or plan to use AI, & fewer than a third of employees use it even inside adopting firms (GOV.UK, 2025). But early doesn't mean neutral. The gains are already flowing upward. A country can get richer on paper while most of its workers see nothing.
So when Witherspoon tells women to catch up, she's addressing a real problem, individual access, while completely missing the structural one. Upskilling helps individuals, but it can't by itself undo a system concentrating returns upward. That's where policy has to do the heavy lifting.
The Invisible Economy: Care as Social Infrastructure
If AI amplifies whatever economic structure it sits on top of, then the question becomes: what does that structure systematically undervalue? And the answer, once you look at the numbers, is staggering.
Women & girls perform over three quarters of all unpaid care work on the planet. Twelve and a half billion hours of it every single day. Oxfam calculated that at minimum wage, not a living wage, just the legal minimum, that labour is worth US$10.8 trillion a year (Oxfam, 2020). The ILO puts the equivalent figure at roughly 9% of global GDP. To make it even more concrete: 708 million women worldwide are locked out of the formal labour force entirely because they are doing unpaid care instead (ILO, 2018). Not because they don't want to work. Because someone has to look after the children, the elderly, the sick, & that someone is overwhelmingly female.
Bring it home to the UK & the picture doesn't soften. Carers UK puts the UK-wide number at 5.8 million unpaid carers contributing £184 billion a year. The ONS went further in their 2023 household satellite account & valued all unpaid household services, cooking, cleaning, childcare, the lot, at £1.7 trillion. That is 61% of GDP. Think about that for a second. Work equivalent to 61% of the entire measured economy, happening every day, completely unpaid.
And the paid care sector isn't much better off. I've seen vacancy figures that should alarm anyone paying attention. In England specifically, adult social care had about 1.595 million filled posts in 2024/25 & roughly 111,000 vacancies, a 7% vacancy rate, worse than both the NHS & the wider economy (Skills for Care, 2025). Median hourly pay? £12.00 in March 2025. Fifty-six pence above the National Living Wage. By December it had crept to £12.60, but by then 48% of the independent sector workforce was already earning below the incoming April 2026 minimum of £12.71. One in five residential care workers lives in poverty, according to the Nuffield Trust. & demographic projections say we need another 470,000 care posts by 2040 just to stand still. The Health Foundation estimates England is short £3.4 billion simply to meet existing publicly funded adult social care demand by 2028/29.
Mariana Mazzucato explains the mechanism behind this in The Value of Everything (2018). She argues that somewhere along the way, economics started confusing price with value. If something commands a high market price, we assume it creates value. If it's unpaid or poorly paid, we assume it's worth less. Care got stuck on the wrong side of that line. It is foundational to every other sector. Without it, nothing else functions. But because no one invoices for it, it doesn't count.
When people turn up to paid work, that's usually because someone else is carrying care responsibilities at home. AI-boosted productivity in a law firm or a tech company is built on top of care that nobody paid for. Pull that foundation away & the formal economy goes with it.
Which is exactly what happened during COVID. The UK lost 232,000 women from employment between early & late 2020 (ONS). Women were a third more likely than men to be in sectors that shut down (IFS). Mothers were 1.5 times more likely than fathers to lose their job or quit by May 2020. Inside households, women picked up 64% of the housework & 63% of the childcare during lockdowns, even in couples where both partners were still working. One in six working mothers cut their hours. PwC calculated that the UK fell five places in its Women in Work Index, wiping out progress back to 2017 levels.
COVID made this painfully obvious. It exposed how fragile the care system already was. Carers didn't fail; the system failed after years of depending on unpaid labour as spare capacity.
People Who People Better
There is a deeper argument here that goes beyond economics, although it has profound economic implications. Care is essential to the development of human beings, & it is not something we should be looking to outsource to a machine. Children, older people with dementia, & people in crisis all need human presence, not automated scripts. You can't learn empathy from an algorithm or find comfort in a chatbot when you're frightened & confused. These needs are stubbornly, irreducibly human.
If an economy can't reward what society genuinely needs, it's mispriced. Right now, it rewards processing data, generating reports, reviewing contracts, managing logistics, all things machines are about to do better than people ever could. The value of those white-collar tasks is going to fall, & honestly it should, because a machine will do them faster, cheaper, & more consistently. I don't see that as tragedy; I see it as a shift in what should be valued.
What emerges from this reallocation is a clearer division between machine work & people work. Machines will outperform us on a growing range of tasks, just as factories did. But people will always people better than machines can. Raising children, comforting the bereaved, sitting with someone through pain, teaching a young person that they matter, holding a hand at the end of a life. Calling this "low-skill" misses the point entirely; care work is technically & emotionally demanding in ways no benchmark captures.
An economy that pays its data analysts well & its care workers poverty wages has its values inverted. When AI compresses the value of cognitive routine work, the practical response is to reprice care properly, not panic about every white-collar shift. We need to put a higher value on people who people better. That's the point of the Care Dividend: route a slice of AI-era gains into care capacity.
The Feminist Case: Meet Women Where They Are
I'm not interested in purity tests here. I know what the end game looks like: women having more options & more choices about how they live their lives. One of the most direct ways to create those options is to address the economic inequities that exist right now. I'm talking about today's labour market, not an imagined future equilibrium.
Women are already doing this work. That 76% isn't a forecast; it's current reality. Eight trillion dollars a year of unpaid labour, performed overwhelmingly by women, feeding directly into the functioning of every economy on the planet. Paying for care doesn't trap women; it gives real economic choice where none currently exists. Financial recognition for labour they are already performing means they are no longer economically penalised for doing it. That is a precondition for genuine choice, not an obstacle to it.
I am fully supportive of women getting into STEM. It matters enormously. But the conversation about women & AI cannot only be about the women who are positioned to enter technology careers. It has to include the millions of women who aren't in STEM & aren't going to be, not because they lack ability but because they are doing something else that society depends on. Supporting women into AI careers & supporting women who are raising children, caring for elderly parents, & maintaining households aren't in conflict; we need both. A feminism that only speaks to women who can code leaves most women out of the conversation entirely.
This is where I should be honest about my own position, because it shapes how I think about all of this. I would be the first person to tell you how unethical big tech is. I also teach people how to leverage their products all of the time. That looks contradictory, but most of us haven't picked the system we get to participate in. You wake up one day & your job, your bank, your doctor, your children's school, all of it lives inside a handful of platforms that you had no say over. That's not an individual failing; it's how the system is built. The majority of people have had very little practical option to not participate. The trap isn't using the tools; it's that opting out can cost you income, access & basic participation in daily life.
There are people who engage in purity politics around this, who will tell you to just disconnect, just refuse, just walk away. That advice only works if you already have the money, the time, & the safety net to absorb the cost. The people most harmed by these technologies are usually the ones with the fewest alternatives. I cannot change who wins or loses at a macro scale, but I can put my time & energy into helping everyday people navigate what's in front of them. Telling someone who is struggling that you have a better vision for the future feels hollow if that vision doesn't meet them where they are today. Until individuals can practically & affordably seize the compute, literacy is the liberation.
The same logic runs through the care argument. You cannot tell women currently performing eight trillion dollars of unpaid care that a more equal future is on its way & expect that to mean much while the bills go unpaid. You have to meet them where they are: already doing the work, already absorbing the cost, already holding the system together.
The objection I hear most often is that paying women for care risks "entrenching the gender division of labour." I understand the concern. If the policy amounts to "here's some money, keep doing what you're doing," then yes, it could reinforce the expectation that care is women's work. But that objection, taken to its logical conclusion, argues against compensating women for work they are already doing, on the grounds that the ideal future looks different. In the meantime, real women are absorbing real costs. You cannot ask a generation of women to subsidise the economy for free while you wait for a redistribution that shows no sign of arriving on its own.
Policy has to run on two tracks at once: compensate current care work & reduce the future imbalance. That means paying care workers a living wage, which would draw more men into the sector through simple economics. Better childcare & respite services to lift the unpaid burden. Pension & national insurance credits so time spent caring doesn't kill a lifetime's earnings. & yes, supporting women into STEM, technology, & every other field where they're underrepresented.
The gender pay gap in the UK is not an abstraction. It is partly driven by exactly this dynamic. Women step out of the labour market or reduce their hours to provide care. Their earnings drop. Their pension contributions drop. Their career progression stalls. The gap widens over a lifetime. Even women who return to full-time employment rarely recover the lost trajectory. Compensating care directly addresses this at the point where the damage occurs, rather than attempting to fix it downstream through equal pay legislation that cannot reach unpaid work.
Compensation should follow caregiving itself, not gender labels, because the objective is to value the work regardless of who does it. But honesty requires acknowledging that the overwhelming majority of beneficiaries would be women, because women are the overwhelming majority of carers. That isn't a flaw; it's the policy matching the labour split we actually have.
Lessons from the Nordics
If the objection is that none of this can work, the Nordic countries have already demonstrated that much of it does.
Sweden spends 3.32% of GDP on family benefits. Denmark spends 3.15%. Norway spends 2.78%. The United Kingdom spends 1.85%, less than the OECD average of 2.35% & roughly half of Sweden's commitment (OECD Family Database, Indicator PF1.1, 2021 data; individual country figures derived from OECD Social Expenditure Database). The UK ranks 23rd out of OECD nations on this measure.
The results are visible in labour market outcomes. Female labour force participation in Sweden is 61.68%, in Norway 61.72%, & in Denmark 59.77%, compared to 57.28% in the UK (World Bank/ILO, 2024). The gender gap in employment is smallest in Iceland, Finland, & Sweden, where it falls below three percentage points.
Sweden's parental leave system offers 480 days per child, with 90 days reserved for each parent that cannot be transferred to the other. If a father does not use his 90 days, they are lost. The first 390 days are paid at 77.6% of qualifying income. Denmark guarantees universal childcare from 26 weeks of age, with municipalities covering at least 75% of operational costs & low-income households paying nothing at all. Over 90% of Danish three-to-five-year-olds are in formal childcare (Danish Ministry of Children & Education, 2025).
Nordic care workers are paid significantly more than their UK counterparts. In Denmark, average care worker pay is approximately £17.20 per hour, compared to the UK median of £12.60, a gap of roughly 37% (Skills for Care, 2026; Nordic Council of Ministers, 2025). This is driven by sectoral collective bargaining agreements covering 80 to 90% of the workforce. In the UK, adult social care is a largely privatised, fragmented market with weak collective bargaining. The structural difference in how care is organised explains much of the pay difference.
The Nordic model is funded by general progressive taxation. Tax-to-GDP ratios range from 40% to 45%, compared to roughly 33% in the UK (Tax Foundation, 2023). There is no earmarked AI levy or equivalent.
From this, two practical points matter most. The outcome I'm proposing already exists in countries with comparable economies. So the issue isn't affordability in the abstract; it's political willingness. & Nordic evidence actually bolsters the case for an AI-linked funding route in UK conditions. The Nordic countries built their care infrastructure over decades through high general taxation. The UK is not starting from that base & is unlikely to reach it through across-the-board tax rises in the current political environment. An AI dividend offers a route to begin closing the gap by attaching new revenue to new economic gains, rather than asking for higher taxes on existing incomes.
The Arithmetic: A UK Test Case
I want to be honest about what the numbers allow & what they don't.
Before the numbers: what follows are transparent scenario calculations against the government's own upper-bound estimate, not revenue forecasts. The care spending figures are England-only (Skills for Care, Health Foundation, IPPR), while the AI productivity estimate is UK-wide, so there is a geographic mismatch that slightly overstates what any England-specific policy could capture. I flag this because honesty about assumptions matters more than clean arithmetic.
The most useful official benchmark is the UK government's claim that AI could be worth up to £47 billion a year to the economy over a decade, if gains are fully realised (GOV.UK, 2025). That figure comes with caveats. The government's own labour market assessment says it is derived from assumptions, not from observed data, & that adoption remains modest. The Department for Science, Innovation & Technology notes that only around one in five firms currently use or plan to use AI. £47 billion is a ceiling scenario, not a prediction. But it is the government's own upper bound, & it gives us a transparent base for arithmetic.
At 5%, 10%, & 25%, you're looking at roughly £2.35 billion, £4.7 billion, & £11.75 billion a year if those upper-bound gains materialise. A deliberately conservative case, where only one quarter of the estimated gains is actually taxable & the levy is 10%, still yields approximately £1.175 billion.
That last figure, the conservative one, would cover the net £330 million annual cost estimated by IPPR for paying all adult social care workers in England the real Living Wage more than three times over. It would not transform the care economy, but it would make a measurable difference to the lowest-paid workers in one of the most strained sectors in the country.
The £4.7 billion from a 10% levy at the full upper bound would exceed the Health Foundation's estimate that England needs an additional £3.4 billion to meet publicly funded adult social care demand by 2028/29. It would equal roughly a tenth of current combined public spending on adult social care (£34.5 billion) & early years support (£10.5 billion, per IFS).
The £11.75 billion from a 25% levy would slightly exceed current public childcare support spending. That is not small money by any measure.
But even the full £47 billion, if every penny were hypothetically redirected, would only cover about a quarter of Carers UK's £184 billion UK-wide estimate for unpaid care, & roughly 3% of the ONS estimate for all unpaid household services. No, this won't monetise all care. Yes, it can fund a meaningful recurring reform package.
The practical offer is annual, material reform funding, not total coverage, linked to a revenue stream that grows as AI adoption grows.
There is also a fiscal multiplier argument. Research by De Henau & Himmelweit (2021), published in Feminist Economics & commissioned by the Women's Budget Group, found that investment in care creates 2.7 times as many jobs as the equivalent investment in construction. For women specifically, care investment creates 6.3 times as many jobs. Crucially, even for men, care investment creates 10% more jobs than construction, not fewer. The same research found that 50% more tax revenue is recouped by the Treasury from care investment than from construction spending, & that care investment produces 30% less greenhouse gas emissions.
Expanding the care workforce to 10% of the employed population, which is where Sweden & Denmark already are, & paying all care workers the real Living Wage, would create approximately 2 million jobs & increase overall employment by 5 percentage points (De Henau & Himmelweit, 2021). This is redistribution, yes, but it's also high-yield job creation with a better return than the infrastructure spending governments typically favour.
What a Workable Model Looks Like
The tax base should not depend on proving that a specific algorithm replaced a specific worker. The UK government's own assessment says the current evidence does not give clear policy answers on causality, & that exposure to AI is not the same as adoption. Taxing each automation event would be a bureaucratic mess & analytically flimsy.
The IMF has been explicit on this point. A specific robot or AI tax is not advisable because it would be hard to administer & could suppress productive adoption (IMF, 2024). That critique knocks out a tool-tax model, not a profits-based levy. The IMF's own recommendation is stronger corporate & capital income taxation, including supplemental taxes on excess profits, as the mechanism for ensuring AI gains are broadly shared.
In practice, the levy should attach to observable rents: excess profits of large AI providers & AI-intensive deployers, strengthened taxation of capital income, & the removal of corporate tax features that artificially accelerate labour displacement through software or hardware investment.
This is less radical than it sounds. HM Revenue & Customs already administers several targeted corporate levies: the Bank Levy, Bank Surcharge, Residential Property Developer Tax, Energy Profits Levy, & Electricity Generator Levy. The precedent is clear. When profits are unusually concentrated or arise from strategic economic shifts, the UK tax system can & does impose sector-specific or windfall-style charges. An AI dividend applied to concentrated AI rents would be novel in subject matter, but it would not be administratively alien.
Design-wise: make it graduated, set a real threshold, & link reliefs to augmentation outcomes. The levy should capture supernormal returns without penalising firms making modest use of AI to improve ordinary operations. Pair it with enhanced capital allowances for AI investments that demonstrably create new roles or improve working conditions, so the incentive structure favours keeping people in work rather than replacing them.
The IMF's own analysis warns against blunt approaches. Their fiscal policy work argues that broad-based capital taxation, while effective at reducing inequality, can also suppress output & wages if set too high or applied too indiscriminately (IMF, 2024). The practical lesson is clear: skim concentrated rents, do not broadly penalise productive adoption. The slogan "tax AI labour" becomes much stronger once it is translated into "tax AI rents."
One practical risk is cross-border leakage. The IMF's 2026 scenario note warns that AI can reinforce winner-take-most market structures & that taxation requires international coordination to limit cross-border spillovers. A national government cannot assume all rents created by domestic AI use will appear neatly inside its own tax base. Any serious version of this policy therefore requires both domestic legislation & engagement with international tax coordination efforts, including the OECD BEPS framework that is already addressing related questions in digital taxation.
Where the Money Goes
Without a spending plan, revenue projections are just headline maths. The Care Dividend should be split between two objectives: recognising unpaid care & building paid care capacity.
On the unpaid care side, the most immediate reform is Carer's Allowance. The 2026/27 rate is £86.45 per week, with an earnings limit of £204. If a claimant provides 35 hours of care, the minimum qualifying threshold, that works out at roughly £2.47 per hour of care. That isn't a wage in any serious sense; it's token support. Reforming Carer's Allowance by raising the rate, increasing the earnings limit so carers are not penalised for also working, & extending eligibility to those currently excluded would be one of the highest-impact, lowest-cost interventions available.
On the paid care side, the priority is straightforward: wages. If care workers are paid poverty wages, you cannot recruit enough of them, & the ones you have leave. You do not solve a recruitment crisis in a sector with a 7% vacancy rate & over 111,000 unfilled posts by running advertising campaigns. You solve it by paying people properly. The IPPR estimated that paying all adult social care workers in England the real Living Wage would cost a net £330 million per year, less than 2% of the social care budget. That is within reach of even the most conservative revenue scenario modelled in this paper.
Beyond immediate wage reform, the spending framework should include: childcare expansion, because inadequate childcare is one of the primary mechanisms through which women are pushed into unpaid care & out of the labour force; respite services, because unpaid carers who burn out become healthcare costs; & pension or national insurance credits for time spent caring, because the long-term earnings impact of caregiving is one of the main drivers of the gender pension gap.
The core logic is straightforward: pay care labour fairly, expand capacity, & stop caregiving from wrecking lifetime income. Do all three using revenue that grows as the AI economy grows.
Answering the Obvious Objections
I have had these conversations enough times to know what comes next. So let me go through the predictable pushback & explain why most of it doesn't hold up.
The first thing people say is that taxing AI will kill innovation or scare investment out of the UK. This gets wheeled out every time anyone proposes taxing concentrated wealth, & it deserves a direct answer. Yes, a badly designed robot tax could suppress useful investment. The IMF says as much. But I'm not proposing a tax on the tool. I'm proposing a tax on supernormal profits from it. Those are different things.
The UK already does this. The Bank Levy has been running since 2011. The Energy Profits Levy arrived in 2022. Neither caused banks or energy companies to pack up & leave. They adjusted, because they need access to the UK's consumer market, its regulatory environment, its talent, its legal infrastructure, & its financial ecosystem. AI companies need the same things. They are not going to relocate to a low-tax jurisdiction that offers none of that because Britain started skimming a share of extraordinary profits. The companies that threaten to leave over taxation rarely do. The ones that actually leave were never committed to the domestic economy in the first place. & if the worry is about small firms, the answer is straightforward: set the threshold high enough that it only touches the biggest & most profitable deployers. Startups don't have supernormal profits to tax.
People also ask why I don't just advocate for raising general taxation. I could. The Nordic countries fund their care systems that way & it works brilliantly. But they built that tax base over decades, with tax-to-GDP ratios between 40% & 45% compared to Britain's 33%. No UK government is going to raise income tax by twelve percentage points to match Denmark. An AI dividend is politically viable in a way that across-the-board tax rises are not, because it attaches new revenue to new economic gains rather than asking people to pay more from existing incomes.
Then there's the question of why care specifically, rather than just general welfare. Because the care bottleneck isn't abstract. It is concrete, measurable, & already causing damage. Over 100,000 vacant posts. A 7% vacancy rate. A projected need for 470,000 more workers by 2040. A £3.4 billion funding gap just to meet existing demand by 2028/29. Care isn't one worthy cause among many. It is one of the economy's binding constraints. On top of that, every pound invested in care creates 2.7 times more jobs than the same pound spent on construction, & generates 50% more in tax revenue back to the Treasury (De Henau & Himmelweit, 2021). It's not just morally right. It's better economics.
Yes, AI training is still part of the answer. Nobody is arguing against skills policy. The IMF backs it, & so do I. I spend a significant portion of my working life helping people build AI skills. But skills alone cannot solve unequal capital ownership, unequal access to tools, or the unpaid care burden that already shapes women's choices long before AI enters the conversation. Training is necessary. On its own, it is not enough.
And yes, AI can help the care sector. The best use case is cutting paperwork, scheduling friction, & routine admin so workers can spend more time with actual people. The WHO's position is that AI should enhance health, not substitute for human relationships. I agree completely. You don't want a chatbot sitting with your gran. You want a human being doing that, properly supported & properly paid. AI should be making that possible, not replacing it.
The hardest objection is timing. Care commitments are permanent & recurring. AI revenue is uncertain & potentially volatile. The UK government's own review admits that the evidence is still emerging & that major policy questions remain open. Fair enough. That means the design has to be conservative: start with a modest levy, build a reserve fund in the early years, & maintain a floor funded from general taxation so that if AI revenue underperforms, care workers & carers aren't the ones left exposed. The worst possible outcome would be hiring thousands of care workers on the back of optimistic projections & then defunding their posts two years later.
Conclusion: Human Potential
In 2015, I ended a talk with a line that has stayed with me: no matter the revolution, the most valuable resource has been, & always will be, that of human potential.
I was talking about digital inequality. About young people locked out of the economy by barriers they didn't create. About a system that measured success by individual achievement while ignoring the collective structures that make achievement possible. I said we are interdependent beings in an interdependent society, & that needs to be reflected in the values we impart.
Having spent years in youth work watching young people try to navigate a system that wasn't built for them, & then years in AI consulting watching the same dynamics repeat at a larger scale, I keep landing on the same question. It's never just "what can the technology do?" The real test isn't technical capability; it's distribution: who gains, who pays, who carries the hidden labour.
Reese Witherspoon asked women to catch up. The evidence suggests that catching up, on its own, won't close the gap. The gap is structural. It requires a structural response.
I'm not pitching utopia; I'm pitching a fundable policy route with known trade-offs. The Care Dividend takes a share of concentrated AI gains & puts it into the work the entire economy depends on but refuses to pay for. The maths is plausible, the policy precedents are real, & the evidence base is strong enough to act.
The facts are already in front of us: care is enormous, mostly done by women, persistently underfunded, & economically miscounted. AI is generating concentrated returns that will grow over the coming decade, & without deliberate policy those returns will flow to the people who already have the most.
Technology has always driven & shifted what society values. But what I keep coming back to, after a decade of working in this space, is that regardless of whether someone is neurodiverse or neurotypical, a bright mind or bang on average, whether they struggle or soar, we should be cultivating economic systems that serve all people, all intelligences, all capabilities, both mental & bodily, with people contributing what they fundamentally can, & with society meeting the basic needs of every single individual. If a system can't meet that baseline, it's failing its purpose.
The Care Dividend is a step toward a system that is not obsolete. It prioritises social good & socially acceptable outcomes over the unchecked concentration of technological gains. An AI economy should be productive, yes, but also visibly more humane in outcomes. & the most direct way to get there is to fund the work that is already, by definition, the most human thing we do.
The feminist response to AI isn't "learn faster." It's building an economy that values what women already do. Tax concentrated AI rents & channel the proceeds into paid care capacity & fair caregiver support.
Declaration of Interest
The author is a practising AI & automation consultant who works with organisations on AI implementation & adoption strategy. This paper argues for taxing concentrated AI profits, which could affect the sector in which the author operates. That position is declared in the interest of transparency. The argument is made because the author believes it is correct, not in spite of working in the field but because of it.
AI Use Disclosure
AI tools were used to assist with research compilation, citation verification, & early structuring during the preparation of this paper. All arguments, analysis, policy positions, & conclusions are the author's own.
Funding
This research received no external funding.
Data Availability
All data cited in this paper are drawn from publicly available sources listed in the references. No original datasets were generated.
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