Over the past two decades, technological progress in Chinese manufacturing has usually been described as a steady march toward greater efficiency: CNC machines reduced manual machining, robotic arms spread through welding, painting, and material handling, MES systems digitized production rhythms, machine vision took over repetitive inspection, and now generative AI and humanoid robots are advancing from both the cognitive and physical sides of work. From the firm’s perspective, the logic is straightforward. Machines can reduce labor input per unit, stabilize production, and move more knowledge into software, databases, and equipment, making production less dependent on any one specific person. The harder question lies on the other side of that process: firms are also becoming less dependent on particular workers, while housing, food, transport, health care, education, and retirement security still depend heavily on wages.

Two different labor disputes in mainland Chinese manufacturing in late summer 2026 help frame the problem. Xingyu Automotive Lighting later disclosed that it had signed employment agreements with 440 members of the 2026 graduating class during its autumn 2025 and spring 2026 campus recruitment cycles, and that 372 eventually entered formal employment. In August 2026, the company entered job-adjustment and workforce-reduction discussions with 140 newly hired employees; 107 ultimately left the company. Xingyu later acknowledged “errors in judgment and hasty decision-making” and said it had “wrongly decided to transfer or reduce positions for some newly hired graduates.” Those affected included R&D, manufacturing, quality, and project roles. What disappeared for them was not only an immediate wage but also an entry point for building the first, second, and third years of occupational experience.Report on Xingyu’s corrective statement

A few days later, a video from a battery factory drew national attention. Early online accounts incorrectly identified the site as CATL’s Yibin operation and linked the incident to alleged restroom restrictions. CATL denied that the event occurred at its facility. On September 10, REPT BATTERO confirmed that the video came from its Jiashan base. The company said the employee had requested immediate resignation processing, that the company believed the procedure required more time, that the two sides failed to agree on timing, and that the employee then engaged in what the company described as extreme behavior. REPT BATTERO also denied the restroom allegation, said police had become involved, and later promised to improve internal employee-communication mechanisms.Yicai report on the Jiashan incident

The available public record is not strong enough to present the restroom allegation as established fact. But the company’s own account still reveals something important: a resignation dispute that should ordinarily have been handled through workplace communication and labor-relations procedures escalated into an extreme incident on the shop floor, police involvement, and a public corporate apology. Modern factories can measure downtime by the minute, output by the unit, and continuously digitize staffing, takt time, quality, and cost. Workers’ ability to leave, rest, and change occupational paths should be treated as equally concrete institutional questions.

This essay is therefore not about the slogan “Will robots take our jobs?” It is about a longer chain of change. As machines get cheaper, firms can more easily reduce their dependence on particular workers. Workers, in turn, may try to remain inside the wage system by taking on more machines, broader job scopes, slower wage growth, and weaker exit options, effectively lowering their unit price. Once technology begins reducing the number of jobs themselves, the question moves further: if the total number of human labor hours society needs keeps falling, how should working time, unemployment, income, social insurance, and the reward for doing necessary work change with it?

1. Time saved by machines does not automatically become workers’ time

Manual loading and unloading around CNC machines is one of the clearest places to see this. CNC systems already control tool paths, spindle speeds, feed rates, automatic tool changes, and much of the cutting cycle. Yet operators may still move workpieces, clamp them, load and unload them, measure parts, inspect machines, respond to alarms, clear chips, and walk between multiple machines. If a machine can cut unattended for ten minutes, that only means the operator no longer has to guide the tool continuously for those ten minutes. It does not mean those ten minutes now belong to the worker.

Production management can simply recalculate that interval as capacity for another machine. One operator who used to handle eight machines may later handle twelve, sixteen, or more. A job that once meant loading and unloading can expand to include material replenishment, cleaning, simple quality checks, and basic inspection. Equipment becomes more automated while the worker still sells the same eight-hour day. The number of machines, tasks, and exceptions covered in each hour increases. Automation reduces labor input per unit of output, but the worker may receive almost none of the time saved.

That is why “machines per operator” is a poor standalone measure of progress. Twenty machines with automated loading, centralized monitoring, lightweight parts, and low failure rates are fundamentally different from twenty machines that still require frequent manual lifting, clamping, measurement, and alarm recovery. Actual workload depends on takt time, machine spacing, workpiece weight, loading time, measurement frequency, simultaneous alarm probability, recovery time, and whether the worker can safely leave the station for a few minutes.

The U.S. Occupational Safety and Health Administration offers a useful example in its restroom rules. OSHA requires employers to provide prompt access to toilet facilities when needed and to avoid unreasonable restrictions. On continuous production lines, employers may use relief-worker systems, but enough relief coverage must exist to prevent unreasonable waiting times. In other words, continuous production is treated as an engineering constraint, not as a reason to make human biological needs absorb all organizational costs.OSHA: Restrooms and Sanitation Requirements

California has carried the same principle into warehouse productivity regulation. AB 701 restricts production quotas that interfere with meal periods, rest breaks, restroom use, or occupational safety. In 2024, the California Labor Commissioner cited Amazon for violations of the Warehouse Quotas Law at two facilities, issuing penalties totaling $5,901,700 and again emphasizing that hidden quotas should not pressure workers to skip meals, rest, or restroom access.California DIR: Amazon Warehouse Quotas penalties

These rules accept the engineering reality of continuous production without forcing the human body to absorb every scheduling constraint. A minute saved by a machine can become profit, capacity for another machine, or an actual minute less of human labor. Technology only creates the possibility of needing less labor. Who receives the saved time remains a question of labor relations and institutions.

2. Automation enters wage bargaining before it replaces anyone

Productivity gains tell us how much one worker can now produce. Another change is equally important: how costly is it for the firm to lose this particular person? In this essay I continue to call that change a “substitutability dividend.” SOPs, CNC programs, failure databases, MES systems, standardized processes, and AI knowledge systems move experience out of individuals and into organizations. Workers still matter, but the degree to which any one worker is irreplaceable can decline.

Sylvain Leduc and Zheng Liu directly examine the connection between automation and wage bargaining in a 2024 paper in the American Economic Journal: Macroeconomics. Their abstract states that “the threat of automation weakens workers' bargaining power in wage negotiations.”AEA: Automation, Bargaining Power, and Labor Market Fluctuations

The key word is “threat.” A robot does not have to be standing next to the worker already. Once automation moves from a trade-show concept to something procurement can price and finance can evaluate, it becomes a credible outside option for the employer. The old threat — “if you will not do it, someone else will” — begins to acquire another layer: “later, we may not need another person at all.”

Workers’ response does not have to appear as an explicit wage cut. A monthly wage can remain unchanged while one person’s machine count rises from eight to twelve. A production role can absorb replenishment, quality checks, and basic maintenance. Tasks once split between two people can gradually be combined. The nominal wage is the same, but each unit of wage buys more labor and output for the employer. I use “defensive repricing” for this process: the number on the paycheck stays the same while the unit price of labor falls.

Fan, Hu, and Tang studied Chinese firm data from 2004 to 2012 and found that, in the 2008–2012 sample, a 10% increase in the minimum wage was associated with a 0.11 percentage-point increase in a firm’s probability of adopting robots, against an average adoption probability of roughly 0.19%. The effect was stronger among more productive, coastal, privately owned, and skill-intensive firms.ScienceDirect: Labor costs and the adoption of robots in China

That evidence shows that relative labor cost can influence automation decisions. It does not imply that the answer is to keep human labor permanently cheap. Technology costs keep falling. The cost advantage workers regain today by taking responsibility for more machines can be eroded by the next generation of equipment a few years later. Firms can wait another year for a robot to become cheaper. Rent, groceries, and household expenses do not pause for a year. The thinner the safety net, the less time workers can afford to wait and the more pressure they face to accept a lower effective price for their labor.

Unemployment insurance therefore affects bargaining power even before unemployment occurs. A worker who knows that job loss will not immediately destroy the household budget has more room to refuse sharply deteriorating hours or conditions than someone who cannot make next month’s rent without the next paycheck.

3. Human-to-human replacement was industrialized before robots arrived

Constant recruitment in manufacturing does not necessarily mean constant expansion. A plant that requires 1,000 workers at steady state can lose 60 workers every month and hire 60 replacements. That produces 720 hiring events in a year while net employment ends exactly where it began.

Fieldwork by Liu and Zhu on manufacturing labor markets in the Yangtze River Delta documents a chain of labor companies, intermediaries, and recruitment agents built around factories’ need for flexibility. In one industrial-park district alone, the researchers found around 150 intermediary companies. They write that the business model and strategies of the intermediary chain can be “consistently aggravating labor turnover.”Springer: Why workers’ turnover is so high

This infrastructure changes the cost of turnover. When a skilled worker leaves and takes months to replace, management has a strong incentive to ask why people are leaving. When intermediaries can keep supplying replacements and standardization shortens ramp-up time, turnover becomes a flow problem — measured through attrition rates, refill speed, and recruitment bonuses — rather than necessarily a reason to redesign the job itself.

Xingyu’s disclosures show how labor input can be recomposed. At the end of 2024, the company reported 10,426 employees, including 7,911 production workers and 1,734 technical staff. At the end of 2025, total headcount had fallen to 7,532, with 4,729 production workers and 2,080 technical staff. Outsourced labor hours rose over the same period from roughly 8.64 million to 10.87 million hours. These figures cannot prove that specific employees were replaced one-for-one by outsourced labor, nor can they isolate the effects of automation, orders, and restructuring. They do show that the company can rebalance labor among formal production employees, technical staff, and outsourced work.Xingyu 2025 annual report, CNInfo

Xingyu’s 2024 annual report also contained an obviously implausible entry in the field labeled total remuneration paid for outsourced labor. That episode is a useful reminder: disclosure is not the same as reliability. An automated economy does not mainly need more dashboards. It needs data systems in which headcount, outsourced hours, wages, productivity, recruitment, turnover, and technology spending can be cross-checked against one another.

Standardization lowers the technical cost of replacing people. Labor intermediaries lower the transaction cost. Robotics lowers the physical labor requirement. These are not unrelated developments; they form a continuous chain that strengthens firms’ ability to substitute away from particular workers.

4. Firms want experienced technicians, but someone still has to fund the first year of experience

A technician who can independently troubleshoot PLCs, servo systems, electrical diagrams, robots, complex downtime, and quality problems does not emerge fully formed on graduation day. That person has to begin with inspection routines, basic alarms, and assisted maintenance, then accumulate the first year, the second, and the third.

Training creates a familiar externality. Company A can spend two years developing a novice into an experienced worker, only for Company B to hire that worker later at a higher wage. For an individual firm, reducing general training and buying mature experience from the labor market can be rational. When many firms behave this way, the industry as a whole ends up complaining that experienced workers are scarce.

Singapore’s Skills Development Levy offers one practical response. Employers contribute SDL for employees working in Singapore, currently at 0.25% of monthly wages, with a minimum of S$2 and a maximum of S$11.25 per worker. The CPF Board states the purpose plainly: “SDL is used to support workforce upgrading.” The levy goes into the Skills Development Fund and supports workforce training.CPF Board: Skills Development Levy

The significance lies less in the specific rate than in the principle. A company can train internally or hire from the market, but the industry cannot make its contribution to skill formation fall all the way to zero simply by choosing not to train.

Germany’s dual vocational system addresses another part of the problem: portability. Apprentices train in both firms and vocational schools, while final qualifications are certified through legally recognized external structures such as chambers. Skills built in Company A therefore remain legible in Company B. Leaving the current employer does not reset occupational competence to zero.

Automation makes this more urgent because robots often enter at the bottom rungs of occupational ladders. BMW says Figure 02 operated in real production at its Spartanburg plant for ten months, supporting production of more than 30,000 BMW X3 vehicles, moving more than 90,000 components, and accumulating about 1,250 operating hours in tasks such as handling sheet-metal parts before welding.BMW Group: Figure 02 production project

Hyundai has laid out an Atlas deployment roadmap as well: beginning with parts sequencing in 2028 and extending into component assembly by 2030, with further expansion into repetitive, heavy, and more complex work. Its own wording is: “Beginning in 2028 … parts sequencing. By 2030, applications will extend to component assembly.”Hyundai Motor Group: Atlas roadmap

These jobs are not always the most technically advanced, but they have often served as entry points through which younger workers learn parts, machines, production rhythms, safety, and quality systems. Once the first rung is automated away, the industry must deliberately build a new first rung. Otherwise job ads will increasingly demand “three years of robot-maintenance experience” while fewer employers are willing to provide year one.

5. Employment adjustment may first appear as “not hiring” rather than layoffs

The employment effects of stronger tools are unlikely to wait for a single dramatic day on which robots replace everyone at once. A more plausible sequence is already visible: the same workforce uses stronger tools and completes more work; firms then hire fewer novices; retirees and leavers are not replaced at the old rate; total human hours decline gradually over several years.

The ILO’s 2025 global update estimates that around one in four workers are in occupations with some degree of exposure to generative AI and emphasizes that, because many jobs still require human input, “most jobs will be transformed rather than made redundant.” That describes the immediate task-level relationship, but “the occupation still exists” does not mean “an occupation that once required ten people will still require ten people.”ILO: Generative AI and jobs: A 2025 update

Stanford Digital Economy Lab, using ADP payroll data covering millions of U.S. workers and updating through June 2026, finds that employment among workers aged 22–25 in highly AI-exposed occupations stands about 19% below where it would have been if it had tracked less-exposed peers of the same age. The researchers also write that adjustment “appears to operate primarily through reduced hiring of young workers rather than increased separations.”Stanford Digital Economy Lab: Canaries in the Coal Mine?

The study explicitly treats these as descriptive patterns, so the 19% gap should not be presented as a clean causal effect of AI alone. Its strongest implication is different: technology can reshape employment before headline unemployment rises, simply because young people are not hired in the first place.

Manufacturing can adjust in the same way. Experienced workers stay until retirement, the company does not replace them, routine tasks gradually move to robots, and the remaining workforce covers more machines and exceptions. Headcount falls over several years without a single dramatic mass-layoff event.

Volkswagen’s 2024 restructuring agreement in Germany offers a concrete reference point. The company plans to reduce more than 35,000 German positions by 2030 using negotiated restructuring, natural attrition, retirement, and changes in hiring. Protecting incumbent workers from immediate mass layoffs does not mean future entry points for young workers remain unchanged. Technology-related employment adjustment therefore has to be tracked not only through layoffs but through novice hiring, apprenticeship places, replacement rates for retirees, and total labor hours.

6. The path to a highly automated society will not jump directly from a 40-hour week to “nobody works”

The first response to higher productivity should be less excessive labor. While society still needs most of the working-age population, the first time dividend from technology can appear as less overtime, shorter standard hours, and higher hourly pay. At this stage the core issue is still the CNC problem: some of the ten minutes the machine has already saved should actually return to the worker instead of being immediately assigned to another machine.

As aggregate labor demand falls further, work-sharing becomes more important. Germany’s Kurzarbeit shows that cutting hours can prevent a sudden decline in labor demand from turning into an equivalent decline in employment. At the 2009 peak, roughly 1.5 million workers were on short-time work arrangements, and the OECD has estimated that the system may have preserved as many as about 580,000 jobs.OECD: German short-time work case

Germany was responding to a demand crisis rather than long-run automation, but the distributional logic is useful. If a society’s required labor falls from 4,000 hours to 3,200, it can keep 80 people at 40 hours and push 20 people to zero, or it can spread some of the reduction in hours more broadly. Technology does not dictate the first choice.

Eventually, however, shorter hours reach a limit. If 100 people once worked 40 hours each for 4,000 total hours, reducing everyone to 20 hours can absorb a long-run decline to 2,000 required hours. If the economy eventually needs only 1,000, insisting that every working-age adult must still hold a conventional job begins to manufacture work for the sake of preserving the old social form.

The transition will therefore create more intermediate states. Someone may work three days a week and study on another day. A worker may spend six months in structured education after several years of employment. A parent may reduce market work during a period of family care. Some people may use AI, software, and digital fabrication for small-scale independent production. Others may genuinely have periods with no market job. These states cannot become stable if health care, pensions, and basic living standards remain tied almost entirely to a standard full-time contract.

Once market labor demand becomes clearly smaller than the working-age population, forcing everyone to compete for a shrinking number of full-time jobs recreates the central problem of this essay. If 100 working-age adults face only 50 full-time-equivalent jobs and the other 50 still need a job to secure a normal standard of living, the price of labor is pushed downward. Lower wages, broader job scopes, and worse conditions become bargaining chips for access to those 50 positions.

A highly automated society therefore has to change the role of wages in the income system. Wages gradually move from being the sole gateway to normal life toward being a clear additional reward for providing scarce labor, personal time, and responsibility. If nursing, maintenance, grid operations, complex equipment, safety, or emergency response remain difficult to staff, the answer should be higher hourly pay, shorter hours, and better leave — not deliberate poverty for people without jobs so they can be coerced into accepting unattractive work.

High automation does not mean human beings do nothing. Fewer market jobs and fewer socially valuable activities are entirely different things. Care, learning, art, community participation, research interests, small-scale production, and human relationships can all remain abundant. More of those activities simply cease to require a conventional employment contract as the price of admission to normal life.

7. Income protection must keep people from falling through the floor while still making work clearly pay more

A real fairness problem appears here. Suppose two people once earned similar incomes. One continues commuting, working eight hours a day, meeting performance targets, and carrying responsibility. The other temporarily has no market job but receives almost the same — or even more — disposable income. It is reasonable for the worker who continues working to feel that something is off. Society cannot ask someone to surrender dozens of additional hours every week while assigning almost no additional economic value to that time.

The United States’ 2020 pandemic unemployment expansion offers an extreme case. The CARES Act added a flat $600 per week in Federal Pandemic Unemployment Compensation on top of regular state benefits. NBER researchers estimated that from April through July 2020, 76% of eligible regular unemployment-insurance recipients had statutory replacement rates above 100%, with a median of 145%. Even after accounting for taxes and employer-provided nonwage benefits, 69% still had replacement rates above 100%.NBER: US Unemployment Insurance Replacement Rates During the Pandemic

The pandemic also involved lockdowns, health risks, care obligations, and jobs that had simply disappeared, so those figures should not be reduced to the claim that “high benefits made people refuse work.” They illustrate a more basic design issue: if cash income outside work remains close to or above wages for long periods, workers who continue to provide time and responsibility can reasonably conclude that their additional contribution is not being priced clearly.

Income protection therefore has to work on both sides. People who lose jobs cannot be pushed into poverty after a few weeks, because workers who desperately need next month’s rent will accept almost any low-priced job and thereby weaken bargaining power across the labor market. But people who continue doing necessary work also need a visibly higher total return through wages, pension accumulation, and other benefits.

Singapore’s Workfare Income Supplement addresses the second side. WIS supplements lower-income workers through both cash and CPF payments. By February 2026, more than S$12.9 billion had cumulatively been paid to 1.13 million lower-income workers. The CPF Board states: “Workfare is paid in the form of CPF payments and cash to supplement their incomes.”CPF Board: Workfare Income Supplement

Canada’s Employment Insurance “Working While on Claim” provisions address another problem: the benefit cliff. Within defined limits, each dollar of earnings reduces EI benefits by only 50 cents. In the government’s current example, someone receiving C$330 per week in EI takes a part-time job paying C$350 and still retains C$155 in EI, raising total weekly income to C$505. Returning to some work clearly raises total income instead of causing the entire benefit to disappear.Canada.ca: Working While on Claim

Technology-driven transitions also create another type of loss: a worker can be “reemployed” while earning much less. Someone who earned 8,000 in a mature technical role may find only a 6,000 job in a new sector. Conventional statistics count that person as employed again, but the household has experienced a real downward step. The United States’ former Reemployment Trade Adjustment Assistance program used time-limited wage insurance for some trade-displaced workers, partially covering the gap between previous and new earnings. A similar mechanism can be useful in technology transitions: the payment goes to the worker, is time-limited and phases out, allowing people to build experience in a new field without choosing between long-term unemployment at the old reservation wage and an immediate permanent pay cut.

The durable fairness principle for a highly automated society is therefore two-sided: people without market work must not lose the basis of a normal life; people who do market work must receive a clearly higher return for the time and responsibility they provide. The first protects the ability to refuse degrading terms. The second preserves a fair price for labor itself.

8. Social insurance cannot forever assume that almost everyone will spend decades earning wages

Mainland China’s employee pension system still depends heavily on wage employment and contribution histories. A gradual increase in the statutory retirement age began in 2025, eventually raising the retirement age to 63 for men, to 58 for women whose prior statutory retirement age was 55, and to 55 for women whose prior age was 50. Beginning in 2030, the minimum contribution period required for monthly basic employee-pension benefits will also rise by six months per year, moving from 15 years to 20 years by 2039.National People’s Congress: Decision on gradually raising the statutory retirement age

That reform is primarily a response to demographic aging, life expectancy, health, and labor supply. It was not designed for AI or robotics. Yet it provides an important reference point for the future. If the working-age population remains large while the economy requires fewer full-time jobs and fewer total wage hours, relying mainly on “work longer and contribute longer” to stabilize the system will increasingly conflict with the technological trend toward less necessary labor.

A young worker in a future of more interrupted employment, part-time work, long training periods, and repeated occupational transitions may find it harder, not easier, to accumulate a longer minimum contribution history. If society no longer needs everyone to work 40 hours but still needs everyone to remain attached to wage employment simply to finance social insurance, then part of the labor time saved by machines has effectively been reinserted through the contribution system.

This does not mean contributory pensions should disappear overnight. Benefits linked to personal earnings and work histories can still retain a contributory layer. What has to widen over time is the financing base for foundational protections.

Existing systems already offer several directions. Japan finances half of its basic pension through the national treasury. The Ministry of Health, Labour and Welfare states plainly: “基礎年金の財源の半分は国庫負担で賄われています” — half the financing of the basic pension is covered by the national treasury.Japan MHLW: Basic-pension financing

Germany’s statutory pension insurance also does not rely exclusively on wage contributions. Deutsche Rentenversicherung’s 2025 financing figures show roughly 76.9% of revenues coming from contributions and about 22.3% from federal subsidies.Deutsche Rentenversicherung: 2025 financing

France broadens the base further by applying social levies such as CSG and CRDS to parts of property and investment income. That is an important reminder that the tax base for social protection does not have to be identical to the wage bill.Service-Public.fr: Social levies on property and investment income

Mainland China itself already contains another approach with greater relevance to a future in which wage labor may shrink as a share of national income. In 2017, the State Council decided to transfer portions of state-owned capital into social-security funds. Large and medium-sized state-owned or state-controlled enterprises and financial institutions within scope were generally required to transfer 10% of state equity, with dividends and investment returns available to help cover employee basic-pension shortfalls. The policy explicitly described one objective as enabling all people to share in the development gains of state-owned enterprises.Ministry of Finance: Transfer of state capital to enrich social-security funds

That arrangement already contains a logic close to the needs of a highly automated society: social insurance can receive capital income directly rather than relying entirely on wage contributions.

A more stable transition would therefore be layered. Pension and insurance components closely tied to personal employment history can remain contributory. Basic old-age protection, essential health coverage, and part of unemployment protection can gradually receive greater fiscal support. As labor’s share of income falls, capital income, corporate profits, and broader tax bases can carry more of the financing burden. Returns from public and state-owned capital can flow into long-term social-security funds. The financing question gradually shifts from “how much did workers earn in wages?” to “how much income and capital return did the economy as a whole generate?”

9. Shared capital income can reduce the conflict created when only the unemployed receive the technology dividend

If public support linked to technological progress is available only after someone loses a job, society can split into two identities. People who continue working may feel they bear commuting, hours, responsibility, and taxes, while only those who become unemployed gain access to new “automation dividends.” That structure can intensify resentment between workers and nonworkers.

Shared capital income offers another route. Alaska’s Permanent Fund converts part of mineral-resource revenue into a long-term public asset and pays an annual dividend to eligible residents from investment earnings. People who continue working receive the dividend as well, so the payment does not create an incentive structure in which one must first become unemployed to share in common wealth.Alaska Permanent Fund Dividend

Norway’s Government Pension Fund Global uses a much larger intergenerational asset model, channeling the state’s petroleum net cash flow into a fund and then using fiscal rules to support the public budget so that resource wealth benefits both current and future generations.Norway Ministry of Finance: Government Pension Fund

AI and robotics are not natural resources, but the ownership logic still matters. Modern technology industries depend heavily on university research, public infrastructure, energy systems, electric grids, government procurement, and industrial finance. If the public sector retains equity, fund shares, or long-term return rights in some investments, part of the capital return from successful technology can enter public income directly rather than appearing only as compensation after a worker is displaced.

That produces a more coherent income structure. Someone who continues working receives wages plus shared returns. Someone without a market job receives basic protection, insurance, and the same shared returns. Someone in a training period may receive additional study support. The common base is universal, while market labor still commands a clearly additional price.

10. Learning cannot become an endless race away from whatever machines just learned to do

Automation debates often push responsibility back onto individuals. Learn PLCs today; learn robotics tomorrow when diagnostics improve; learn AI after robots become easier to program; then search again for whatever the next system cannot yet automate. A life organized this way is still entirely about proving that one remains worth hiring.

Occupational learning matters, but adults often lack time and income more than course catalogs. Singapore’s SkillsFuture Mid-Career Training Allowance directly adds income support to longer training. Eligible mid-career citizens in full-time long-form training can receive an allowance equal to 50% of average monthly income, capped at S$3,000 per month, for up to 24 months over a lifetime. The official explanation explicitly says the rate is intended both to offset income loss and to preserve “the incentive for them to return to work.”SkillsFuture: Mid-Career Training Allowance FAQ

Sweden’s transition-study support goes further in recognizing that becoming a student again in mid-career can be a normal stage of life. Occupational stability can gradually mean not “remaining in the same employment state for forty years” but “being able to change state without falling out of the income, insurance, and education systems.” Someone may work at 30, study systematically at 35, return to work, later spend time on family care or another round of retraining, and then re-enter the labor market again.

Learning outside the labor market should retain its own value as well. A mechanic can study history, an engineer can learn photography, and a production worker can learn music without first proving that the activity will raise future wages. If productivity really rises, part of the social dividend should be more adult time for understanding the world and pursuing interests that have no immediate market price.

11. Another major meaning of AI is that ordinary people can push ideas through the tool barrier

Generative AI affects employment in another direction that is often neglected: it lowers the amount of professional execution capacity an individual must purchase in order to test a small idea. A website once required substantial front-end, back-end, database, and deployment knowledge. A game required code, art, animation, and sound. A 3D character required modeling, topology, UV work, materials, and rendering. Many ideas never reached the market because they died before the first workable prototype existed.

Brynjolfsson, Li, and Raymond studied 5,179 customer-service agents using a generative-AI assistant and found an average productivity increase of 14%, with a 34% improvement among novice and lower-skilled workers and little improvement among the most experienced high-skilled workers. Their paper writes of “a 34% improvement for novice and low-skilled workers” and provides evidence consistent with AI helping newer workers access practices used by higher performers.NBER: Generative AI at Work

That matters for ordinary workers. The person who actually repairs cars knows which small workflow problems never get fixed. The person who actually operates CNC machines knows which alarm logs and handover forms waste time every day. A small-shop owner knows exactly what simple software the business needs. These people often possess domain knowledge but not a programmer, designer, or product team. As AI lowers the cost of producing a first version of code, websites, images, data tools, or 3D prototypes, the distance between knowing what should exist and being able to build something testable gets shorter.

Time no longer needed for employment therefore does not have to become “idleness.” Some people can build small software tools, open-source projects, characters, games, 3D prototypes, and physical products, using AI, 3D printing, and CNC to test ideas that once required a small team. Problems too small to interest a conventional firm can increasingly be addressed by people who actually live with them.

Real AI empowerment, however, also requires exit capacity. If a person’s model memory, files, prompts, workflows, and project context are locked inside one provider, dependence on an employer can simply be replaced by dependence on a platform. Open weights, open interfaces, data export, context portability, and local execution matter because they make the provider itself replaceable.

As firms become less dependent on any particular employee, individuals should become less dependent on any particular firm. As AI platforms participate more deeply in personal work, users should also become more capable of taking their accumulated context and leaving a particular provider.

12. Care robots should replace burdens before they replace relationships

A highly automated society does not imply that every human activity should be run at the lowest possible staffing ratio. Care work makes this especially clear. Japan has promoted transfer-assistance robots, monitoring systems, ICT, and other care technologies with goals that include improving service quality, reducing physical burdens on caregivers, and supporting older people’s independence. Automating repetitive lifting, delivery, night rounds, and fall monitoring has obvious value.

The decisive question is what happens to the time saved. If a care worker spends two fewer hours on mechanical lifting, those hours can go into conversation, observation of physical and emotional changes, and communication with family members. An institution can also choose the opposite path: reduce staffing and keep the remaining workers fully loaded.

Research on the PARO robotic seal illustrates the distinction. A 2023 systematic review and meta-analysis covering 12 studies and 1,461 participants found some effects on medication use, anxiety, agitation, and depression, but also concluded: “The overall quality of evidence for all outcomes were graded as low.”PubMed: PARO systematic review and meta-analysis

A robot can produce an interaction that genuinely comforts an older person. Care still contains judgment and responsibility. If a patient suddenly stops eating, becomes confused, or faces a high-risk decision, someone still has to connect medical history, medication, past habits, and family context and take responsibility for what happens next.

The right care metrics therefore run in two directions at once. Back injuries, dangerous lifting, repetitive night work, and mechanical chores should fall. Real human interaction, observation, and individualized care time should rise. Robots should first replace the burdens inside care, not compress the relationship itself.

13. Every institution will be gamed, so reality should not be compressed into one beautiful number

Novice-hiring rates, training hours, internal promotion rates, average wages, and formal headcount can all provide useful information. The moment one number directly determines subsidies, ratings, or regulatory outcomes, organizations begin optimizing around the number.

Require a novice quota and firms can add short-term interns. Reward training hours and providers can pile up classes. Reward internal promotions and job titles can change. Reward higher average wages and a firm can raise the average by cutting the lowest-paid workers. Reward retention and employers may gain an incentive to make exit harder.

Reliable monitoring therefore requires multi-year occupational trajectories and several measures that constrain one another. A firm can simultaneously show output up 30%, average wages up 8%, formal employment down 25%, outsourced hours up 40%, and novice hiring down 30%. Those tensions are not statistical noise; they are the reality that needs explanation. Productivity should be read together with actual hours and staffing ratios. Wages should be read with hourly earnings and workforce composition. Training should be read with later external certification and occupational mobility. Employment should be read across formal jobs, dispatch, outsourcing, hiring, and turnover. Entry-level opportunities should be followed for several years to see whether novices actually gain skills.

Institutional maturity does not mean finding a number that cannot be gamed. It means raising the cost of constructing a false story because too many independent measures would have to line up with it.

14. Unions and other organizations change the condition in which one person has to bargain alone against the machine

If one shop-floor worker believes the number of machines assigned to one person has become unsafe, the easiest individual response is to quit. Ten workers who leave separately become ten attrition records, followed by ten replacement hires. If the same ten workers jointly collect data on walking distance, workpiece weight, loading cycles, simultaneous alarms, exception handling, and injuries and then demand a staffing discussion, the issue changes from individual endurance to an engineering arrangement management has to explain.

The 2010 Nanhai Honda parts strike in China illustrated how bargaining power changes at the collective level. Any one young production worker could look highly replaceable. Once a critical parts supply stopped collectively, multiple Honda assembly plants were affected. Individual substitutability and collective substitutability are not the same economic fact.

Automation makes this kind of organization more important before deployment, not only after job losses. After Hyundai published its Atlas roadmap, the company’s Korean union publicly demanded labor-management agreement over robotic deployment. The important questions arise before layoffs: which processes receive robots first, which retiring positions will no longer be refilled, how many machines remain per worker, and how productivity gains affect wages and hours.

Firms already act collectively through the corporate form, management hierarchies, finance teams, legal departments, and industry associations. If ordinary workers can appear only as isolated resignations, injuries, or individual complaints, the gap in organizational capacity widens as technology makes people easier to substitute. Unions, industry bodies, media, academic researchers, NGOs, religious welfare organizations, and other intermediary institutions therefore remain important even in a society with stronger automation and social protection. Capital requires oversight; the administrative systems that distribute public money and capital returns require oversight as well.

15. In a truly highly automated society, full employment gradually gives way to full income security, participation, and choice

Abandoning full employment today would be unrealistic. Housing, food, health care, education, and retirement security still depend heavily on wages. When an ordinary person loses a job, purchasing power and real options genuinely shrink. Saying “human value is not measured by wages” does not solve that material reality.

But there is also no reason to treat “every adult working full-time for roughly 40 hours a week” as an eternal social objective. In the early stages of productivity growth, overtime and work intensity can fall. Next, shorter hours can spread a reduction in aggregate labor demand. Later, work, study, care, and personal projects can cycle through the same life. If market production eventually needs only a relatively small amount of human labor, wages can increasingly become an additional reward for doing necessary work rather than the sole basis of a normal life.

That path avoids two bad extremes. One deliberately impoverishes people without jobs in order to force them to accept work, creating a larger pool of cheap labor. The other makes disposable income under work and nonwork so similar that people who surrender large amounts of time feel their labor has no value.

The more stable principle is simple: people without market jobs should not lose a normal standard of living; people who take on market work should clearly receive more; and especially scarce, dangerous, or high-responsibility work should attract people through higher hourly pay and shorter hours.

A highly automated society is therefore not necessarily a “mass-unemployment society.” It is a society in which more valuable activities no longer have to be converted into full-time employment contracts before people can secure the material basis of life.

Conclusion: what machines should really reduce is the amount of life people are forced to sell

Industrial society built a stable exchange: people sell time and labor to firms; firms pay wages; people use those wages for housing, food, transport, education, health care, and retirement. Wages therefore serve both as the market price of labor and as a gateway to the resources required for normal life. When those two functions are tightly bound together, every improvement in machines’ ability to substitute for labor can feel like a threat to a person’s right to live normally.

That pressure begins before a machine formally replaces anyone. One person covers more machines. The same pay absorbs more responsibilities. Rest time shrinks. Wage growth slows. Employment relationships become more flexible for the employer. Firms gain stronger substitution options while workers remain constrained by monthly household expenses. Machines get cheaper; people first respond by lowering the effective unit price of their own labor to remain inside the wage system.

Once technology starts reducing the number of jobs themselves, society often responds by telling workers to learn whatever machines cannot yet do and find another 40-hour job. That cycle raises productive efficiency but fails to deliver the most basic promise of productivity growth: maintaining the same standard of living should require less necessary human labor over time.

A real transition has to move on several fronts at once. Skills must become portable across firms. Workers who lose jobs need enough waiting time to avoid panic repricing. Some decline in labor demand should first appear as shorter hours. New entrants still need ways to build the first year of experience. People who continue working need clear additional income. Social insurance must become less dependent on a single wage-tax base by drawing more from general revenue, capital income, and public capital returns. And the time released by machines should be able to flow into care, learning, creation, civic participation, or simply remain unassigned.

Mainland China already contains two contrasting tendencies within the same social-insurance system. One is to raise retirement ages and gradually lengthen minimum contribution periods, keeping workers attached to the contribution system for longer. The other is to transfer part of state-owned capital into social-security funds so that capital itself bears part of the pension burden. The first has its own logic in responding to today’s demographic aging. The second has greater long-run relevance if wage labor becomes a smaller share of the economy. A truly highly automated society cannot indefinitely preserve a system built around mass wage labor simply by requiring people to work longer.

Production systems can keep becoming less dependent on key individuals. A factory should not stop because one veteran technician leaves. A software system should not lose all organizational knowledge because one programmer resigns. That is genuine engineering progress.

Human social position should move in the opposite direction: as firms become less dependent on any particular worker, society should make workers less dependent on any particular firm.

What machines should eliminate is repetitive, dangerous, and compulsory labor — and part of the life people currently have to sell simply to qualify for a normal standard of living.

The worst future is one in which machines get cheaper and people, in order to remain employable, have to make their wages, rest, health, and time cheaper too.

A better future is the reverse: machines get cheaper while human time becomes more valuable. As technology produces more wealth, ordinary people become able to sell a smaller share of their lives without losing the material basis of a normal life.

Sources and original material