We're Running Out of Time — AI, Democracy, and the Economy

By Noreen Hynes B.Comm, FCA • June 2026

We are at the brink of something unprecedented — not just another industrial revolution, but a fundamental rethinking of work itself. The governance window is narrowing fast: Congress has failed to pass comprehensive AI legislation despite 183 AI-related bills since ChatGPT's launch,[1] and 72% of US adults already report concerns about AI.[2] According to the Pew Research Center, a median of 34% of adults across 25 countries are more concerned than excited about the increased use of artificial intelligence in daily life. A median of 42% are equally concerned and excited, and 16% are more excited than concerned.[3] People are concerned, and so should their political representatives.

While many people are genuinely excited about what AI can deliver for humanity — in medicine, education, and beyond — a growing unease exists about who is steering it and to what end. Reid Hoffman, co-founder of LinkedIn, has estimated that more than 50% of Silicon Valley billionaires have acquired what he calls "apocalypse insurance" — properties and hideaways in New Zealand and other remote locations — driven in part by fear that AI-driven job displacement will trigger social unrest. When the very people building these systems are quietly preparing for the consequences of their own creations, governments and citizens alike are entitled to ask why.[4]

"In the simplest terms, empires amassed extraordinary riches across space and time, through imposing a colonial world order, at great expense to everyone else."― Karen Hao, Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI [5]

Many founders of AI companies are warning about the dangers of uncontrolled AI development, yet governments do not appear to be addressing the lack of guardrails. Many experts believe that AI will create a significant financial bubble in the years ahead, when it becomes clear that all the billions spent on AI infrastructure will not be rewarded with adequate returns on investment any time soon. The winners will be the companies that use AI and the startups that are raising massive funds and creating new billionaires. Many question the adequacy of global governance around AI and why governments, except for a few, are not addressing these issues with greater urgency.

The development of AI offers many significant benefits, especially in medical research, education, and automation. Still, the speed of progress and the scale of redundancies are causing great concern. It could widen the wealth gap in society, which has been widening for the past 30 years. Governments cannot ignore the pace of AI development and the potential consequences of displacement. Those now dealing most directly with the consequences of rapid AI progress are Gen Z — highly educated young people who cannot get on the career ladder. They have to pivot and acquire AI skills to secure employment and work alongside AI. Many have left university with large student loan debts and no careers. They need free education, partial or full loan forgiveness, and income support, or they risk becoming the lost generation. So what are the implications of rapid AI development? Join the conversation and read on.

The Scale of Disruption: What the Experts Say

Bill Gates has been candid about what lies ahead. During an appearance on The Tonight Show with Jimmy Fallon in February 2025, he predicted that in the next decade, humans will no longer be needed "for most things" in the world, as expertise once thought rare will become "free, commonplace" through AI.[6]

Geoffrey Hinton, the Nobel Prize-winning "Godfather of AI", is equally direct. Speaking on the Diary of a CEO podcast in June 2025, he argued that jobs performing mundane tasks will be taken over by AI first — roles like receptionists and customer service representatives are already vulnerable. That, he warned, will wipe out a high number of roles immediately:[7]

"You'd have to be very skilled to have a job that it couldn't just do."— Geoffrey Hinton, Diary of a CEO podcast, June 2025

The numbers support this. The World Economic Forum's Future of Jobs Report 2025 projects that by 2030, 92 million jobs will be lost, but 170 million new ones will be created — resulting in a net gain of 78 million jobs.[8]

The problem is that there could be a significant gap: many of those made unemployed will not retrain due to their age or interests, and unemployment could rise sharply while at the same time there could be a shortage of skilled workers.

Goldman Sachs predicts that innovation related to artificial intelligence could displace 6–7% of the US workforce if AI is widely adopted.[9] We are already seeing this: unemployment among 20–30-year-olds in tech-exposed occupations has risen by almost three percentage points since the start of 2025.[9] McKinsey's foundational 2017 analysis — still the most comprehensive modeling of its kind — warned that between 400 and 800 million people globally may need new jobs by 2030, with 75 to 375 million requiring a complete change in occupational category.[10]

What makes these numbers concerning is the speed of change and the fallout that follows.

Big Tech companies reduced graduate hires by 25% from 2023 to 2024, with entry-level roles increasingly displaced by AI.[11] Major banks have moved fast — Morgan Stanley cut 2,000 roles in March 2025, partly due to AI automation, while Bloomberg Intelligence projects up to 200,000 Wall Street jobs at risk.[12]

Hinton, in a wide-ranging interview with the Financial Times in September 2025, placed this squarely in its political context:[13]

"What's actually going to happen is rich people are going to use AI to replace workers. It's going to create massive unemployment and a huge rise in profits. It will make a few people much richer and most people poorer. That's not AI's fault, that is the capitalist system."— Geoffrey Hinton, Financial Times interview, September 2025 (as reported in Fortune, September 6, 2025)

As time goes on, we will observe how trends change and how quickly AI advances. It is still unclear how many jobs will be lost and what those losses will mean, but from the research carried out to date, doing nothing is not an option for governments. They have a duty to recognize when a seismic shift is happening, and early action is essential.

The Verification Question: Are the Job Loss Forecasts Accurate?

Before accepting the headline numbers, a critical question demands an answer: if AI outputs require human verification to be trustworthy — and the evidence strongly suggests they do — then AI does not eliminate the need for human expertise. It transforms it. The person who used to produce the work becomes the person who verifies it. That is a different job, but it still requires the same underlying knowledge.

The major forecasts largely measure task exposure — the percentage of tasks within a job that could, in theory, be performed by AI — rather than net employment outcomes, which are far harder to model. Research from Harvard Business School finds that generative AI is simultaneously displacing skills in automation-prone roles while expanding skill requirements in roles where human judgment, oversight, and verification remain essential.[14]

Microsoft's 2025 Work Trend Index reports that AI is reshaping work by automating routine tasks while increasing the value of human judgment, oversight, and decision-making. At the same time, labour market data show strong growth in demand for AI-related skills, with employers increasingly seeking workers who can combine technical proficiency with analytical thinking and digital literacy.[15]

History reinforces this scepticism. In the 1980s, spreadsheets were predicted to eliminate accounting jobs. Accountant numbers rose. ATMs were predicted to eliminate bank tellers. Teller numbers increased for two decades as lower branch costs allowed more branches to open. Legal document review software was predicted to eliminate junior lawyers. It shifted their work toward supervising the software and handling exceptions. In each case, the tool eliminated specific tasks but increased the demand for human judgment in those tasks.

The jobs most genuinely at risk are not the expert roles the forecasts emphasise. They are high-volume, low-judgment processing roles in which output requires no expert verification and errors are tolerable or catchable by automated systems. The jobs most protected are those in which someone must stand behind the output — in medicine, law, finance, and publishing — where liability frameworks and regulatory requirements ensure that human accountability cannot simply be outsourced to a machine. Goldman Sachs' own equity research team has noted that most enterprises have yet to generate any returns from their AI spending, and that the overall AI investment dynamic is "unprecedented and unsustainable" until enterprise adoption proves its value.[16]

Some AI researchers warn that if artificial general intelligence arrives before adequate safeguards are in place, the consequences for humanity could be irreversible, with one striking analogy comparing the fate of less intelligent species in a world dominated by a more powerful intelligence.

Who Profits? The Return on AI Investment

The scale of AI investment is extraordinary. Microsoft alone committed approximately $80 billion to AI-enabled data center infrastructure in fiscal year 2025.[17] The six largest technology companies collectively spent an estimated $200 billion on capital expenditures in 2025, up from $110 billion in 2023. Goldman Sachs Research estimates that tech giants and beyond are set to spend over $1 trillion on AI capital expenditure in the coming years.[16] & [17]

Yet the companies building AI are not profitable. Internal OpenAI documents predict the company is set to lose fully $14 billion in 2026, with total losses projected to reach $44 billion by 2029.[18]

There is one clear winner: Nvidia. Its full-year fiscal 2025 revenue reached $130.5 billion — more than double the prior year — with gross margins of 75% and net income of $72.9 billion.[19] Goldman Sachs Research observed that most of the economic value generated during the current AI investment cycle has accrued to semiconductor companies, particularly Nvidia and other suppliers of the infrastructure required to build AI systems, while model developers and hyperscale cloud providers continue to spend heavily and have yet to demonstrate sustainable returns on their investments.[20]

Goldman Sachs Head of Global Equity Research Jim Covello argued that AI must solve genuinely complex business problems to justify its enormous costs and that current generative AI systems are not yet designed to do so. In the same report, MIT economist Daron Acemoglu projected only limited US economic benefits from AI over the coming decade, challenging some of the industry's most optimistic forecasts.[21]

The productivity gains from AI are largest in high-value knowledge work — legal, financial, medical, and consulting. But these are precisely the domains where human verification remains structurally necessary. That means AI in these fields functions as an accelerant for existing experts, not a replacement for them. An accelerant is valuable — but the value flows primarily to the expert and their clients, not to the AI company, which still only charges a subscription fee.

The historical pattern is instructive. During the 1990s Internet boom, Cisco, Sun, and Oracle were the clear infrastructure winners for years. The transformative value of the internet was real, but most of the companies that built the foundational infrastructure went bankrupt. The value was captured by those who built services on top of it a decade later: Amazon, Google, Facebook. Today's AI infrastructure builders may be laying the foundation on which others, not yet founded, will build the most profitable applications.

For workers, the implication is uncomfortable but important. The people making the largest fortunes from AI are doing so primarily through equity gains in companies that are not yet profitable and may never generate returns proportionate to their investment. The workers displaced by AI in the meantime will bear the real costs of a transition whose benefits may accrue overwhelmingly to a small group of equity holders and infrastructure suppliers. That is precisely why the taxation and redistribution framework proposed in this article is not a marginal policy adjustment. It is a structural necessity.

The Purchasing Power Paradox and Wealth Concentration

AI is creating wealth at unprecedented speed. In the US, there are now 498 AI unicorns — private AI companies with valuations of $1 billion or more — totaling $2.7 trillion in combined value, according to CB Insights. Fully 100 of them were founded since 2023. There are more than 1,300 AI startups with valuations of over $100 million. "Looking back over 100 years of data, we have never seen wealth creation of this magnitude and speed," said Andrew McAfee, co-director of MIT's Initiative on the Digital Economy. "It's unprecedented."[22]

Meanwhile, AI-driven productivity gains favour high-income workers, intensifying inequality. Acemoglu and Restrepo's landmark research documents that between 50% and 70% of changes in the US wage structure over the last four decades are accounted for by relative wage declines of worker groups specfavorialised in routine tasks in industries experiencing rapid automation.[23]

This creates a "demand crisis" — when large groups cannot buy, the economy stalls regardless of how efficient production is. AI risks accelerating this process significantly, leaving no income to replace that of those who have lost their jobs. This is not like the Industrial Revolution because the pace of change is so fast — in months rather than years.

AI, Democracy, Governance, and Taxation Systems

Keeping Democracy Intact

The threats include the collapse of the information ecosystem through AI-generated misinformation, enhanced surveillance capabilities, and unprecedented wealth concentration — AI has driven such extraordinary wealth creation that San Francisco now has more billionaires than any city on Earth, with 82 compared to New York's 66, according to New World Wealth and Henley & Partners — and reduced civic engagement due to economic stress.[22] However, AI can also improve government services, enhance policy analysis, increase transparency, and foster better democratic participation.

California passed SB 53 on September 29, 2025, the first US statute focused on frontier AI safety and catastrophic risk incident reporting.[24]

Yet federal policy has moved in precisely the opposite direction. The Trump administration has issued three executive orders on AI that together represent the most significant federal intervention in AI governance since the technology emerged — and none of them address worker displacement, wealth concentration, or economic fairness.

First, on January 23, 2025, President Trump signed Executive Order 14179, "Removing Barriers to American Leadership in Artificial Intelligence," which immediately revoked President Biden's 2023 AI safety framework. Biden's order had required AI companies to conduct safety testing, protect against bias and discrimination, and report dangerous capabilities to the government. Trump's replacement reframes AI development as a matter of national competitiveness, stating that US policy is to "sustain and enhance America's global AI dominance" through systems "free from ideological bias or engineered social agendas." Every federal agency was directed to identify and remove regulations considered barriers to AI innovation.[25]

Second, on December 11, 2025, President Trump signed Executive Order 14365, "Ensuring a National Policy Framework for Artificial Intelligence," which established a Department of Justice AI Litigation Task Force specifically tasked with challenging state AI laws in federal court. California's SB 53, Colorado's AI Act, and similar state-level protections are now actively under federal legal threat. A March 2026 National Policy Framework followed, recommending Congress pass legislation to override conflicting state laws entirely. Having removed federal AI regulation, the administration moved to prevent states from filling the gap.[26]

Third, on June 2, 2026, President Trump signed "Promoting Advanced Artificial Intelligence Innovation and Security," the most recent executive order. Driven by national security concerns that the most powerful AI models had developed the capability to identify and exploit software vulnerabilities at scale, the order asks AI companies to submit their most powerful models for a government cybersecurity review up to 30 days before public release and creates an AI cybersecurity clearinghouse. The review process is explicitly voluntary. Trump had previously scrapped a stricter mandatory version, stating publicly: "We're leading China, we're leading everybody, and I don't want to do anything that's going to get in the way of that lead." The administration clarified that the order is "NOT conducting oversight of all new models."[27] & [28]

The pattern across all three orders is consistent: innovation and national security take priority; worker protection, economic redistribution, and democratic governance of AI do not feature. The most powerful government in the world has had 18 months to respond to the disruption this article documents — and its response contains no mechanism whatsoever to address displacement, wealth concentration, or the demand crisis AI risks creating. That is not a governance gap. It is a deliberate policy choice.

Globally, legislative mentions of AI rose 21.3% across 75 countries in 2024, and in the United States, federal agencies introduced 59 AI-related regulations — more than double the number in 2023.[29] The European Union has also adopted the EU AI Act (Regulation (EU) 2024/1689), the first comprehensive horizontal AI law, which entered into force on August 1, 2024.[30] Progress is happening — but not fast enough.

What Are the Essential Taxation Reforms?

As AI transforms our economy, tax systems must evolve to capture value increasingly created through automation rather than human labor. Leaving the current framework unchanged as robots displace workers is not a policy choice — it is a political failure. Here is a framework that draws on serious academic and institutional thinking, not wishful economics. While the author does not agree with all the opinions expressed in the research, she agrees that you cannot eliminate the returns to innovation through taxation. You have to make the tax system fair, with a decent return for the risk investors take when investing heavily in AI infrastructure that may not deliver returns for years.

1. Progressive Automation Tax (15–25% on salary savings)

A tax levied on companies that deploy AI or robotics to replace human workers, calculated as a percentage of the salary savings achieved. The principle was first proposed publicly by Bill Gates in 2017:

"Right now, the human worker who does, say, $50,000 worth of work in a factory, that income is taxed, and you get income tax, social security tax, all those things. If a robot comes in to do the same thing, you'd think that we'd tax the robot at a similar level."[31]

Drawing on Gates' principle, and on Acemoglu, Manera, and Restrepo's finding that labour is taxed at above 28.5% while effective capital taxes on software and equipment have fallen to approximately 5% — largely the result of favourable depreciation provisions enacted across a series of tax laws from 2002 through 2017 — a reasonable starting point is a progressive automation tax of 15–25% on salary savings achieved through AI deployment, collected quarterly.[32]

The current US tax code actively encourages automation — even when efficiency gains are marginal — by making it significantly cheaper to deploy a machine than to employ a person. Acemoglu and colleagues describe this as "excessive automation": automation driven by tax incentives rather than genuine productivity gains.[33] Michael J. Ahn, Associate Professor at the University of Massachusetts Boston, adds that the tax serves a second purpose: prompting firms to think strategically about when automation genuinely adds value, rather than treating job displacement as cost-free.[34]

No country has yet implemented it at scale, though South Korea capped robot tax deductions in 2017. Bastani and Waldenström (2024) conclude that as AI shifts income from labor to capital, governments must raise capital taxes progressively to fund redistribution.[34]

2. Enhanced Capital Gains Tax

A capital gain is the profit you make when you sell an asset — shares, a company stake, property — for more than you paid. Capital gains tax is what you owe on that profit. Under the current US system, assets held for more than one year qualify for a preferential long-term rate of 0%, 15%, or 20%.[35] The proposed reform introduces a medium-term band:

• Short-term gains (under 1 year): 35–40%

• Medium-term gains (1–5 years): 25–30% — a new band that does not currently exist

• Long-term gains (over 5 years): 20% — same as today's top rate

A worked example. An AI startup founder receives $1 million in shares at launch. Three years later, the company is acquired, and she sells for $40 million — a capital gain of $39 million. At today's 20% long-term rate, she pays $7.8 million and keeps $31.2 million. Under the proposed medium-term rate of 27.5%, she pays $10.7 million and keeps $28.3 million. The government captures an additional $2.9 million from a single transaction — multiplied across thousands of AI exits — creating a substantial revenue stream directed toward the workers displaced by the very technology that generated those gains.

This three-band structure is this author's proposed framework. No government has yet implemented a medium-term capital gains band of this kind. However, the direction of international policy supports the underlying principle. The UK enacted an immediate rate increase in its October 2024 Autumn Budget, raising the higher rate of capital gains tax from 20% to 24% and the lower rate from 10% to 18%, with effect from October 30, 2024.[36] In the US, President Biden's FY2025 budget proposed taxing capital gains at 39.6% for those earning over $1 million, though Congress never passed this.[37] Both moves show that governments are already recognising that our legacy capital gains structures are no longer fit for purpose. In an emerging era of extreme, AI-driven wealth concentration, reforming how we tax these windfalls is moving from a theoretical debate to an urgent policy necessity.

3. Digital Services Tax (3–5% on gross digital revenues)

A tax on gross revenues that large technology companies earn from digital services — advertising, digital marketplaces, and AI-as-a-service — regardless of where the company is headquartered. The European Commission's 2018 proposal documented that digital businesses pay an average effective tax rate of only 9.5% compared to 23.2% for traditional firms.[38]

France pioneered a 3% DST in 2019. Austria levies 5%. Spain, Italy, Hungary, Poland, Portugal, and Turkey have all implemented versions.[39] A 5% EU-wide DST is under active consideration, with estimates suggesting it could generate €37.5 billion annually by 2026, representing nearly 19% of the EU's 2025 budget.[40] The UK introduced a 2% levy, generating £800 million in 2024–25.[41]

4. Wealth Tax (2–2.5% on ultra-high net worth)

An annual tax on accumulated wealth above defined thresholds. In February 2024, Professor Gabriel Zucman presented a proposal to G20 Finance Ministers in São Paulo for an internationally coordinated standard ensuring the effective taxation of ultra-high-net-worth individuals. In the baseline proposal, individuals with more than $1 billion in wealth would be required to pay a minimum annual tax equal to 2% of their wealth.[42]

The author proposed 1.5% on $50M–$500M, 2% on $500M–$2B, and 2.5% above $2B+. In 2026, Senator Warren's Ultra-Millionaire Tax Act proposed a 2% tax on net worth above $50M and a 3% tax on net worth above $1B, projected to raise $3 trillion over a decade.[43]

Spain, Norway, and Switzerland maintain active wealth taxes today. Critics correctly note capital flight risks, which is precisely why the EU Tax Observatory advocates for internationally coordinated rather than unilateral wealth taxation.

5. Data Value Tax (5% of revenue from data monetisation)

A tax of 5% on revenue that companies derive from monetising user data. AI systems are trained on the collective output of billions of people who never consented to have that output monetized by a handful of corporations. The European Parliament's research service observes that users now play "an unprecedented role in companies' value-creation process," with digital services taxes increasingly aimed at the monetisation of user data.[44]

None of these five reforms requires choosing between innovation and fairness. Korinek and Lockwood's framework argues that AI-era revenue should come chiefly from taxing consumption — including AI services at the point of use — while keeping the capital and infrastructure that drive productivity lightly taxed.[45]

The infrastructure for running AI, such as data centres, consumes vast amounts of the Earth's scarce resources, including water and electricity. This is at the expense of ordinary citizens who are paying more than they should for these already scarce resources. While it would be unwise to discourage companies risking their capital to build infrastructure, those who use the services these companies provide should pay taxes.

How AI Assists Tax Collection

AI enables fraud detection through pattern recognition, automates compliance checking, and provides real-time monitoring. Tax authorities are beginning to explore GenAI, though most efforts are still at an early, experimental stage. The most evident area so far has been in improving communication with taxpayers. In Singapore, a virtual assistant answers tax questions in multiple languages and has cut call-centre inquiries by half. Korea has deployed an AI guide to help citizens file and pay taxes. In France, AI can analyse incoming emails and propose draft responses for civil servants to validate.

While these applications are promising, a deeper question arises: can GenAI significantly alter the relationship between governments and citizens? How will it influence how citizens experience and perceive taxation — a politically sensitive process governed by law yet deeply intertwined with social norms and practices?[46]

Despite widespread adoption, most organizations remain in the experimentation or piloting phase, with only around one-third having begun scaling AI across their operations. The scale of demand for AI knowledge across government reflects this gap: more than 14,000 employees from nearly 200 federal organizations registered for the 2024 AI Training Series, spanning 21 sessions and achieving a 92% participant satisfaction rating.[47] & [48]

Are Governments Prepared for AI?

Most governments are woefully unprepared.

Critical Skills Governments Need Now:

• AI Policy Specialists bridging technical capabilities and policy implications

• Data Scientists and ML Engineers to audit private-sector AI systems

• Digital Transformation Officers experienced in large-scale technology change

• Workforce Transition Specialists designing retraining programmes

• AI Ethics and Rights Officers preventing bias and civil rights violations

• Cross-Functional Translators connecting policy silos

Action Timeline:

• Months 1–6: Create AI readiness task forces with Chief AI Officers at every agency

• Months 6–18: Rapid upskilling programmes making AI literacy mandatory for managers

• Years 1–2: Reform procurement processes for faster AI acquisition

• Years 1–3: Establish regulatory sandboxes for safe testing

• Ongoing: Cross-sector partnerships with universities, tech companies, unions, and communities

Experts at the Urban Institute highlight a crucial 18-month window for governments to plan for AI's impact on workers — specifically in the context of apprenticeships and workforce transitions.[49]

What Should Governments Do About AI Job Displacement, The Economy, and Democracy?

Universal Basic Income

A three-year randomized controlled study by OpenResearch gave 1,000 low-income participants $1,000 monthly and found that recipients directed the additional funds toward essentials — food, rent, and transportation. Recipients were 2 percentage points less likely to be employed than control participants and worked an average of 1.3 fewer hours per week, though the study found this reflected greater agency in choosing meaningful work rather than disengagement from the workforce.[50]

The Alaska Permanent Fund provides a real-world precedent: it has paid eligible residents an annual dividend funded by the state's oil and natural gas revenues since 1982. The first dividend was set at $1,000 per resident, distributed on June 14, 1982. Payments have ranged from a record low of $331.29 to a record high of $3,284 in 2022, with the 2025 dividend returning to $1,000 per resident.[51]

Recommendation: Start with a living wage covering necessities, add participation bonuses for education, training, and community service, and adjust regionally for cost differences.

Government Equity Stakes

The US government's August 2025 agreement with Intel — under which the Department of Commerce converted $8.9 billion in CHIPS Act funding into an approximately 10% equity stake — provides a precedent for how the government can take a direct financial interest in critical technology companies.[52] This model should expand:

• Tier 1: Companies developing advanced AI systems should offer the government a minimum equity stake of 10%

• Tier 2: Mass-automation deployers should contribute to sovereign wealth funds

• Tier 3: Critical infrastructure AI systems suppliers should have mandatory government representation on their boards

The Future of Work and Human Purpose

Work is not disappearing — it is transforming. The demand for health professionals and STEM workers is expected to grow by 17–30% by 2030.[53] Hinton notes healthcare's elastic demand, arguing that making doctors five times more efficient would mean five times more healthcare for the same price, with almost no limit to how much healthcare people can absorb.[54]

Jobs AI Struggles to Replace:

• Care Work: face-to-face human interaction for eldercare, childcare, therapy, and teaching

• Creative Problem-Solving: requiring intuition and cultural understanding

• Relationship Building: community organizing, mediation, mentoring

• Skilled Trades: electricians, plumbers, mechanics — facing unpredictable physical problems

Gates identifies three relatively safe professions: coders to correct AI errors, energy experts to manage AI infrastructure, and biologists for creative scientific research.[55]

Education, Motivation, and Inequality

Educational Transformation

Our industrial-era education model is obsolete. Two-thirds of countries now offer K–12 computer science education — double the number since 2019 — and US computing degrees increased by 22%.[56]

Four Essential Pillars:

• Learning how to learn — skills have shorter half-lives

• Human–AI collaboration — providing judgment and oversight

• Interdisciplinary synthesis — connecting ideas across domains

• Emotional and social intelligence — empathy, communication, collaboration

Wealth Inequality and the Middle Class

McKinsey estimates that up to 375 million workers globally may need to switch occupational categories and learn new skills by 2030.[57] Morgan Stanley cut approximately 2,000 jobs in March 2025 — its largest round of layoffs since CEO Ted Pick took over in January 2024 — with some roles eliminated specifically because AI and automation had replaced them, and the bank signaling further AI-driven reductions ahead. A Bloomberg Intelligence survey of chief information and technology officers at 93 major banks found that executives expect a net workforce reduction of 3% over the next three to five years due to AI. This projection amounts to as many as 200,000 Wall Street jobs at risk of transformation or displacement. Goldman's analysis shows that the tech sector's share of the US employment market peaked in November 2022 — when ChatGPT was launched — and has since fallen below its long-term trend. The unemployment rate for 20- to 30-year-olds in tech has risen by nearly 3 percentage points since early 2024, over four times the increase in the overall jobless rate.[58] & [59]

Solutions: guaranteed government support for re-education, profit-sharing mandates, housing supports, universal healthcare decoupled from employment, and comprehensive retraining programs.

The Paycheck-to-Paycheck Crisis

Only 47% of Americans have sufficient savings to cover a $1,000 emergency expense. AI-driven job displacement presents a real financial threat.[60] Goldman Sachs estimates the peak unemployment impact of AI during the transition period will be a manageable 0.5 percentage points, as other industries absorb displaced workers. However, the timeline remains uncertain.[61]

Immediate protections include: emergency income insurance, rapid retraining with living stipends, portable benefits, debt-jubilee provisions for displaced workers, and housing-stability programs that prevent evictions.

What Can Governments Do?

Years 1–2: Emergency income insurance, extensive retraining with stipends, UBI pilots, citizen wealth funds, AI readiness task forces, government workforce upskilling, progressive automation taxation.

Years 3–5: Government equity stakes in AI companies, full implementation of tax reform, universal capital accounts, an overhaul of the education system, restructuring of laborGovernment equity stakes in AI companies, full implementation of tax reform, universal capital accounts, an overhaul of the education system, restructuring of labour laws, and scaled-up government AI deployment.

Years 6–10: Full UBI implementation, recognition of non-market work, democratic AI oversight, and global coordination on AI governance.

Essential Principles: Upholding human dignity regardless of productivity, ensuring democratic control over AI's societal impact, promoting equitable distribution of benefits, and supporting ongoing adaptation.

The Choice Before Us

Hinton has spoken openly about his regret. When he left Google in May 2023, he told The New York Times — as reported by CNN — "I console myself with the normal excuse: If I hadn't done it, somebody else would have."[62]

"I'm in the unfortunate position of happening to agree with Elon Musk on this, which is that it's sort of 10% to 20% chance that these things will take over," he added, calling it still just a "wild guess."[63]

Hinton is not alone in his assessment. Yoshua Bengio — the Turing Award winner who co-pioneered deep learning alongside Hinton — led the 2026 International AI Safety Report, backed by over 30 countries and 100 independent experts, which confirmed that AI systems are already exhibiting early signs of deception and that the pace of risk management is failing to keep up with the pace of capability development.[64]

Stuart Russell, author of Artificial Intelligence: A Modern Approach — the world's most widely used AI textbook — has been one of the most consistent and credible academic voices warning about the loss of human control. In a Brookings Institution analysis published July 2025, his 2014 warning — co-authored with Stephen Hawking and Max Tegmark — was cited as foundational: that superintelligent AI systems could be "outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand."[65]

With these stark warnings from some of the world's leading experts, all governments should be deeply concerned and act to put guardrails in place before it is too late.

Gates expressed optimism that AI will deliver "breakthrough treatments for deadly diseases, innovative solutions for climate change, and high-quality education for everyone," while acknowledging that humans won't be needed "for most things" within a decade.[66]

Pope Leo XIV — the first American pope, a former mathematics major, and a leader who made artificial intelligence the defining theme of his papacy from the moment of his election in May 2025 — brought the full weight of the Catholic Church to this debate on May 25, 2026. In his first encyclical, Magnifica Humanitas, he called for the "disarming" of artificial intelligence, warning that it must be "freed from logics that turn it into an instrument of domination, exclusion, and death." Like nuclear energy, he wrote, AI "must be at the service of all and of the common good." With 1.422 billion Catholics worldwide — and 20% of US adults, approximately 53 million Americans, identifying as Catholic — his voice carries extraordinary moral reach into precisely the debate this article documents.[67] & [68]

What makes the encyclical particularly credible is the breadth of expertise that fed into it. The drafting process, which began in July 2025 at the papal residence in Castel Gandolfo, involved both theologians and technology experts. At its formal presentation at the Vatican on May 25, 2026, the speakers included Chris Olah — co-founder of Anthropic, one of the world's leading AI companies — who called for "moral voices that the incentives cannot bend" and urged religious communities, civil society, academics, and governments to take AI seriously and push events in a better direction. He was joined by Cardinal Pietro Parolin, the Vatican's Secretary of State, Cardinal Michael Czerny, responsible for the Church's social justice work, and theologians Anna Rowlands and Léocadie Lushombo. When the people building AI and the people charged with protecting human dignity are sitting in the same room, agreeing that guardrails are urgently needed, the question is no longer whether action is required. It is only a question of whether governments will act.[69]

The future is not predetermined. We can choose a world where AI amplifies inequality, where democracies are under threat, and a techno-aristocracy dominates — or one where AI liberates humans from drudgery and prosperity is shared. Experts at the Urban Institute identify an 18-month window in which AI is poised to shake up the labor market, presenting challenges and opportunities for businesses, workers, and policymakers — with registered apprenticeships emerging as a key policy response.[49]

We have split the atom, walked on the moon, and connected the world through invisible networks. We can handle this too. The AI revolution will be what we make of it — not what algorithms decide, or billionaires want, but what we, together, choose to build.

,Noreen Hynes B.Comm, FCA is a Chartered Accountant, retired CEO, and serial entrepreneur with 40 years of financial experience. She is the author of the award-winning Start-Up Checklist for Success and the forthcoming Rich Start: The No-BS Money Guide for Your 20s. She writes on AI, startups, economic policy, and personal finance at noreenhynes.com. Follow her @noreenhynesauthor.

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