Different Countries, Different Collapses

The Hollow Middle becomes self-reinforcing when three things happen at once: entry pathways narrow, bargaining power weakens, and growth no longer depends on broad middle-class demand. When AI enters the room, it accelerates these pressures and changes where societies choose to absorb them.

My last instalment was built largely from US data, partly because the American version is easier to see: layoffs, weaker entry-level hiring, consumer frustration, political anger and anxiety about whether work still leads to security. But the country comparison starts from a different premise. The Hollow Middle will not appear everywhere in an American form. AI diffusion is uneven, and so are the societies into which AI arrives.

AI diffusion is not spreading uniformly. It is concentrated in economies with stronger institutions, higher governance capacity and deeper technological infrastructure.

This is consistent with the IMF’s AI Preparedness Index, which assesses preparedness across 174 countries using macro-structural indicators such as digital infrastructure, human capital, innovation and regulation. The countries adopting AI fastest are not, in general, the countries least able to absorb its consequences.

That alone should make us cautious about the simplest version of the argument: that AI adoption is already generating the same middle-class crisis everywhere.

It is not.

The more unsettling pattern is that AI is arriving after several foundations of middle-class reproduction had already begun to weaken.

Across very different economies, households are finding it harder to form, workers are struggling to claim a stable share of growth, and families are less able to assume that tomorrow’s security will be stronger than today’s. Fertility has fallen in most countries. Housing has become less affordable in many advanced economies. Where data is available, labour’s share of growth has weakened or stagnated across several economies that otherwise look institutionally very different.

These indicators should not be read as proof that AI single-handedly caused the Hollow Middle. Fertility is shaped by housing, childcare, culture, urbanization, gender norms and family policy. Housing affordability is difficult to compare globally at the same level of quality. Labour-share data is incomplete in several countries we would most want to compare.

The narrower claim is that these indicators all matter for middle-class reproduction, and several are weakening at the same time. That matters for AI because productivity can rise while the social machinery that turns growth into broad security weakens. In a task-based view of automation, the key question is whether technology only substitutes for labour in existing tasks, or whether it also creates new labour-intensive tasks, occupations and career ladders. AI’s middle-class effect will depend on that balance.

This is why the country story matters. AI is arriving into systems where parts of the social contract were already fraying. In advanced economies, it may compress entry-level professional work faster than institutions can redesign career pathways. In emerging economies, it may threaten a services-led ascent that has not yet delivered for some of the largest young cohorts in history. David Autor’s argument that AI could rebuild middle-class work if deliberately designed around worker expertise is the important counterpoint: the outcome is not technologically predetermined.

The pressure is broad. The exposure is uneven. The next question is where each society allows the pressure to show up.

The same pressure does not appear through the same signal

That is where the country differences begin.

The first dimension is pressure visibility: how publicly legible middle-class pressure becomes. This is a measure of whether strain becomes visible, contestable and socially recognizable. In some countries, pressure moves quickly into public language: labour-market anxiety, political anger, protest, media attention, consumer sentiment. In others, it remains more private or indirect: delayed household formation, lower consumption, migration, precautionary saving, informal work or quiet withdrawal from aspiration.

This is why no single indicator can measure middle-class pressure globally. Unemployment may be the right signal in one country and the wrong signal in another. Fertility may reveal long-cycle insecurity in one system and mostly reflect family policy, housing or gender norms in another. Reported distress may be a genuine alarm in one society and a muted signal in another. Albert Hirschman’s classic distinction between exit and voice is useful here. When people experience decline, they can demand repair, or they can withdraw.

The loudest cases are not simply the most advanced AI economies. In this sample, for instance, pressure is most publicly legible in countries where protest, distress and labour-market strain are all visible at once, such as Spain and South Africa. Japan shows the quiet version: pressure appears more through delayed adulthood, subdued consumption and demographic retreat. China is better read as a mixed or administratively muted case: some pressure appears through household caution, fertility decline and savings behaviour, while public contestation remains a less complete measure of underlying strain.

In high-visibility systems, track public and fast-moving signals: protest, reported distress, unemployment, youth exclusion, hiring, layoffs, real wages, sentiment and political salience. In lower-visibility systems, track indirect or slower-moving signals: fertility, household formation, savings, migration, consumption weakness, internal workplace adjustment and gaps between reported distress and observed behaviour.

This is the first step toward classification. Countries are first grouped by whether pressure becomes public or remains private; whether it is voiced, muted, displaced or withdrawn from view.

Adjustment channels: who carries the cost of AI transition?

The second dimension asks a different question from visibility. Who carries the adjustment when AI starts changing tasks, roles and costs? Firms, institutions, workers, households, new entrants, or those outside formal protection?

In some economies, adjustment falls more heavily on workers and households. Firms can reorganize roles, freeze hiring, reduce headcount, change contracts or shift work arrangements with fewer buffers around workers. The burden of transition is more likely to appear through job insecurity, weaker wage progression, thinner entry-level pathways, retraining costs, career resets or household financial pressure.

In other economies, more of the adjustment is absorbed or negotiated inside firms and institutions. Employment protections, collective arrangements, social insurance and workplace norms can slow the move from task change to job loss.

A third pattern appears where many workers sit outside formal protection. In these systems, firms may still adjust quickly, but the cost is often pushed onto workers more privately. AI-era disruption may appear as underemployment, unstable service work, informal work, migration, or a longer wait for secure jobs.

This is why the indicators are better read as clues about who absorbs the shock. Formal legal constraints, such as redundancy pay and notice periods, show how much responsibility firms carry when they restructure. Collective constraints, such as union density and labour-rights compliance, show whether adjustment is likely to be negotiated. Protection gaps, such as limited social protection and informal employment, show how exposed workers are when adjustment happens.

In this sample, the United States sits closer to the worker-and-household side than most advanced economies. This is because adjustment is highly individualized: firms can move quickly, workers carry more job and income risk, and career transitions often depend on personal savings, mobility and retraining. By contrast, Sweden, Germany and France sit closer to the firm-and-institution side, where adjustment is more likely to be mediated through workplace rules, social insurance or negotiated restructuring.

Emerging markets need a separate reading. In countries like India and Vietnam, large protection gaps mean workers and households may absorb much of the transition outside formal systems. Mexico and South Africa are more mixed: both have significant worker exposure, but also formal institutions that shape adjustment for protected workers. Countries like China and Saudi should also be read carefully, because adjustment may be filtered through state, firm, migrant-labour or social-protection systems.

From dimensions to archetypes

The first two dimensions explain different things. Pressure visibility asks whether strain becomes publicly legible or remains private and indirect. Adjustment absorption asks who carries the cost when AI changes tasks, roles and labour demand: workers and households, or firms and institutions.

Neither dimension says whether a country can solve the problem. A signal can be loud and still lead to the wrong response. A signal can be muted and still trigger action. A system can cushion workers in the short term while allowing career ladders to thin over time.

The following archetypes show where AI-era middle-class pressure is likely to travel first. The matrix is deliberately simple. One axis asks whether pressure becomes public or stays muted. The other asks whether adjustment is carried more by workers and households, or absorbed more by firms and institutions. Countries can move between archetypes as AI adoption, labour-market pressure and institutional choices evolve.

Four ways AI-era pressure travels

Loud Fracture

High visibility · Workers and households absorb more

The United States is the clearest case of Loud Fracture. It is not necessarily the most distressed society in the global data. It is the place where the signal becomes visible quickly and where much of the adjustment is individualized. Recent US layoff announcements show how quickly AI investment can become part of the public labour-market narrative.

Loud Fracture is not only an American pattern. In India, AI could intensify the country’s job-creation challenge, especially as millions enter the workforce each year and services-led mobility becomes more exposed. South Africa is equally revealing: its official unemployment rate rose to 32.7% in the first quarter of 2026, making labour-market pressure impossible to hide. Jordan and Indonesia show fragility of the pathway AI may disrupt tomorrow with high share of informal economy, even where AI adoption is not yet the dominant cause.

Firms can reorganize roles, freeze hiring or reduce headcount relatively quickly. Workers carry more of the risk through job insecurity, weaker benefits, retraining costs, mobility costs and household financial pressure. That is why pressure can move rapidly from the labour market into consumer sentiment, political anger and public debate.

This visibility is both an advantage and a risk. The advantage is that the problem becomes harder to deny. The risk is that politics may reach for faster but weaker answers: protectionism, anti-immigration narratives, or pointing fingers to AI and robots. Redistribution and taxation matter especially if AI raises capital returns faster than wages, but they are not enough on their own. The harder task is to pair redistribution with worker transitions, care infrastructure, wage standards, training systems, school-to-work pathways and competition policy so that AI productivity gains do not simply accumulate at the top.

Loud Fracture guarantees audible stress. The central question is whether leaders use that visibility to rebuild the bargain around work, or whether they spend the political signal on faster narratives that leave the underlying fracture intact.

Private Absorption

Low visibility · Workers and households absorb more

Private Absorption describes systems where pressure is real, but much of it is carried privately by households, workers, migrants, new entrants or people outside strong protection systems. As a result, the signal is less likely to appear first as open political conflict or public labour-market rupture.

China sits closest to this archetype in the current map. Its AI transition is moving quickly and explicitly. Policymakers have framed AI as a productivity engine and a source of new jobs, while also acknowledging pressure on young people, graduates and migrant workers. While China is pushing society-wide AI adoption to create jobs and revive growth, the bet also raises the need for stronger welfare support if roles for workers are automated, reduced or reshaped. The pressure is already visible in indirect ways: household caution, savings behaviour, fertility decline, youth labour-market anxiety, cultural language and weaker consumption. AI adds a cleaner labour-market mechanism to that story. It also presents the policy dilemma: firms are being encouraged to innovate, but not simply to push the transition cost onto workers.

Vietnam, the UAE and Mexico show different versions of the same pattern. Vietnam combines rapid industrial upgrading with a more constrained public signal environment. The UAE shows the organizational-speed version more clearly: it has rolled out AI and robotics into work-permit screening to improve labour-market productivity and skilled-talent matching, while its labour system remains heavily shaped by migrant-worker structures. In Mexico, AI exposure is rising in formal jobs, with an estimated 30% of formal jobs at high risk of automation; at the same time, AI tools may also help informal workers access formal services in the country.

That is the core opportunity and risk of Private Absorption. Because pressure is less publicly explosive, firms can move fast. They can redesign workflows, automate routine tasks, and experiment with new operating models before every adjustment becomes a national political fight. For business, that creates a real transformation advantage: as the unit of innovation, firms can move faster than in systems where every disruption is immediately contested.

But the same feature creates the risk. If households, young workers and migrants carry too much of the adjustment privately, weak signals can accumulate into a larger demand, trust and mobility problem. The system may look stable while workers delay marriage, save more, lower expectations, accept weaker jobs or withdraw from aspiration.

Private Absorption therefore has a sharper business implication than the name first suggests. It can enable faster firm-level transformation, but it also places more responsibility on employers. If firms use AI only to cut labour costs, the burden moves quietly onto households. If they use AI to redesign work, retrain workers, improve matching and build new entry pathways, Private Absorption can become the country archetype that captures the biggest transition advantage.

Negotiated Strain

High visibility · Firms and institutions absorb more

Negotiated Strain describes systems where pressure is public, but adjustment is not left only to individual workers or households. Firms, unions, social insurance, employment rules and public systems all shape how the transition unfolds.

In this archetype, AI becomes part of a broader social bargain. The pressure is already visible across several countries in this group. Spain is seeing early signs of AI-related adjustment: with declining employment in computer programming, telecommunications and information services, even as finance, another AI-exposed sector, continues to grow strongly. Korea points to AI pressure landing first on younger workers and hiring channels, while Brazil’s debate over reducing the working week shows how technology, productivity and worker protection can quickly become political questions about the future of work.

The opportunity is that these countries have bargaining machinery, using firms, unions, employers, public agencies and training systems to negotiate how AI is adopted, where productivity gains go, and how workers move into new roles. However, the risk is that negotiation becomes preservation. These systems can spend their political energy protecting existing jobs, existing rules and existing insiders, while younger workers face fewer entry routes and firms delay adoption. As a result, countries in this archetype may experience a slow loss of competitiveness, weaker productivity growth and a thinner middle-class ladder.

The unique battle in Negotiated Strain is therefore, how to make the bargain forward-looking. If these countries use social dialogue to accelerate reskilling and share productivity gains, they can turn visible pressure into managed renewal. If they use it mainly to defend the old settlement, they may keep the peace while losing the future.

Buffered Quiet

Low visibility · Firms and institutions absorb more

Buffered Quiet describes systems that many countries would normally envy. Firms are capable, institutions are trusted, social insurance is stronger, and adjustment is less likely to become a public rupture.

The advantage is real. These systems have more room to manage AI before it becomes a social crisis. Singapore is the clearest execution-speed example: it is investing heavily in public AI research through 2030, building AI talent pipelines, and expanding worker access to AI learning. Sweden also makes worker-training efforts focused on AI-exposed professional groups. In these systems, AI transition can be organized through firms, public investment and skills systems rather than only through household survival.

But that is also the danger. Because the system cushions adjustment, deterioration can look manageable for too long. Jobs may remain, but promotion slows. Tasks change, but career ladders narrow. Entry-level roles become thinner, but unemployment does not spike. The warning signs are already visible in the broader evidence: the ECB has argued that AI may be creating jobs in the euro area for now, but also noted that more than a quarter of German firms expect AI to lead to job cuts in the next five years. In Japan, AI may reduce labour shortages, but it can still absorb the routine and coordination tasks through which younger workers learn.

This is what makes Buffered Quiet different from its neighbours. Compared with Private Absorption, workers are less likely to carry the burden alone. Compared with Negotiated Strain, pressure is less likely to become immediate political conflict. That gives these countries an enviable advantage: they can act before crisis. But it also creates a specific risk: the absence of visible rupture can weaken urgency.

The unique tradeoff is between cushioning and renewal for countries in this archetype. Cushioning protects workers from abrupt shocks. Renewal rebuilds the pathways into good work. If these systems only cushion, they may preserve today’s comfort while allowing tomorrow’s ladder to thin. If they use trust, firm capability and public systems to redesign work early, they can turn AI adoption into a managed productivity transition rather than a delayed middle-class erosion.

Good news is, Buffered Quiet economies do not fail dramatically. However, they may fail when a comfortable present makes a smaller future politically acceptable.

Different collapses, different warnings

The mistake would be to wait for every country to produce an American-looking crisis. The Hollow Middle will not announce itself in the same language everywhere. Elsewhere, pressure may travel through quieter channels: delayed adulthood, cautious households, weaker consumption, thinner entry routes, overprotected insiders, underprotected outsiders, migration, informality or a slow retreat from aspiration.

These archetypes are therefore best read as warning systems. Countries can move between them as AI adoption, labour-market pressure and institutional choices evolve. The point is to ask where each society sends pressure when the middle begins to weaken, and whether leaders are looking in the right place before the pattern hardens.

AI will test countries through what they already do well. Loud systems may turn visibility into polarization. Fast-moving systems may turn execution speed into private household burden. Negotiated systems may turn bargaining into preservation. Buffered systems may turn cushioning into complacency. Each society has a familiar way of absorbing pain, and that familiarity is part of the danger.

The Hollow Middle may arrive as households expecting less, workers progressing less, firms offering less, and politics learning to contain frustration rather than resolve it. The final failure is when reduced expectations become the new normal.

Cover photo by Donald Wu on Unsplash