A World Without Work

What happens when there’s not enough work for everyone? Automation is forcing us to rethink the value of human labor and the systems that sustain our societies.

1. Machines Will Replace, but Also Complement Human Jobs

As automation advances, it provokes anxiety about job losses. While it's true that machines will replace some roles, they also complement others. This dual effect has been seen historically – for instance, during the Industrial Revolution, when weavers who adapted gained significant benefits, even as many lost their traditional roles.

In the modern era, we see the same pattern. Take lawyers, for example. Algorithms now process legal documents faster than humans ever could. But rather than eliminating lawyers, this frees them to focus on creative and interpersonal tasks like strategizing and advising clients. Automation can assist rather than outright replace.

The larger pattern? Automation not only changes how work gets divided but expands the economy overall. It’s like baking a larger pie – though the slices may shift, more is available for everyone overall.

Examples

• ATMs increased both the number of machines and banking jobs by enabling human tellers to focus on specialized customer needs.
• The Industrial Revolution both disrupted traditional occupations and created opportunities for skilled workers operating new machinery.
• Automation has allowed customer service teams to use chatbots for routine queries, freeing up time for complex or emotional customer interactions.

2. Every Job Faces Automation Risk

Many believe automation threatens only low-skilled, repetitive jobs, but research says otherwise – all kinds of work, from simple tasks to highly specialized roles, can be automated in part or entirely. What determines risk? Whether tasks are defined as “routine” or “non-routine.”

Routine tasks, regardless of their complexity, can often be programmed into algorithms. A secretary's rote data entry, a factory worker’s repetitive assembly line task, or even aspects of financial analysis are all examples of work vulnerable to automation. Non-routine skills, which demand creativity, interpersonal insight, or intricate manual work, have traditionally been safer. However, technological advances are beginning to challenge this safety zone.

Even so, the speed and scope vary depending on the task and sector. Technology doesn't replace work uniformly; it reshapes and redistributes labor, some of which eventually disappears entirely.

Examples

• Eighteenth-century mechanical looms turned weaving from a skilled trade into an operation accessible to untrained laborers.
• Cleaner robots are on the rise, while middle-layer roles, like secretarial work, shrink globally.
• Modern image-recognition AI outperforms human efficiency in sorting and analyzing data or photographs, often in non-routine areas like medical diagnostics.

3. AI Progressed When Computers Stopped Trying to Think Like Us

Early attempts at AI mimicked human thought. For example, programmers tried teaching computers chess by modeling expert strategies. But breakthroughs came only when researchers stopped asking machines to think like humans and instead tasked them to find solutions—even unconventional ones—independent of human logic.

This pivot has allowed AI to excel at astonishing levels. Machines scan enormous datasets for patterns humans may never notice. Algorithms don’t require skill sets that mirror human expertise; instead, they leverage their own computational strengths.

These developments hint at why automation’s impact is growing exponentially – tasks once thought impossible for machines, especially non-routine ones, are increasingly within reach of today’s AI.

Examples

• IBM’s Deep Blue defeated Garry Kasparov in chess, not by copying human strategies but by calculating billions of scenarios rapidly.
• AI image-recognition programs regularly surpass humans in identifying objects or detecting specific conditions.
• Translation software now produces more natural and accurate results without relying on human linguistic analysis.

4. Automation’s Reach and Pace Depend on Geography

AI may revolutionize one country’s workforce while leaving another’s largely intact, at least for now. The uneven pace results from differences in regional priorities, populations, and economies. For instance, Japan, with an aging population and high demand for care work, invests heavily in nurse robots. Meanwhile, countries with abundant low-wage jobs may delay automation because manual labor remains more economical.

This diversity means automation doesn’t unfold uniformly. Some regions automate rapidly where it solves pressing problems, while others move slower due to social or political factors.

This variability complicates the global conversation about automation – while it radically changes some industries, others linger in older models of labor.

Examples

• Japan employs social robots like “Pepper” to handle patient interactions and assist in hospitals, driven by their aging population.
• Crop-spraying drones handle over 90% of farming duties in technologically advanced nations, leaving some manual tasks in less-developed economies.
• Manufacturing is automated faster when labor costs rise in wealthier nations but remains human-centric where cheap labor is available.

5. Technology Will Erode Low- and Mid-Skilled Jobs

Automation doesn’t just complement human work – in many cases, it renders certain skills irrelevant altogether. This leads to large-scale job losses. Even when new roles emerge, displaced workers often encounter barriers like mismatched skills or overwhelming competition for retraining.

Low- and mid-skilled positions are most at risk, as these frequently involve tasks that machines readily replicate or exceed humans in doing. Over generations, this could result in fewer job opportunities for entire sectors, with economic output growing while human roles decline.

The emerging reality: human labor will matter less in a larger array of industries, with potentially long-term, global repercussions.

Examples

• Driverless cars are already replacing professional taxi drivers instead of merely assisting them with tools like GPS navigation.
• Translation software now frees companies from relying on low-cost human translators for most documents or casual services.
• AI customer service chat tools reduce demand for entry-level or temporary phone operators.

6. Automation Increases the Wealth Gap

Automation’s economic benefits aren’t distributed equally. Wealth now concentrates among those who own assets like intellectual property, land, or machinery. Many people, who primarily rely on wages, find their earnings stagnant or shrinking as their work is automated out of existence.

Growing inequality reflects a broader economic reality: human labor is worth less than ever. Unless systems change, automation will intensify disparities, benefiting only a select group of workers and owners.

Left unchecked, this growing gap threatens social cohesion, creating a society where the few enjoy exponential growth while many struggle to stay afloat.

Examples

• Between 1980 and 2014 in the USA, the top 1% saw their incomes skyrocket while earnings for everyone else stagnated or declined.
• The richest 1% of Americans now own 40% of total wealth, while the poorest 50% own just 2%.
• Similar disparities are evident in various wealthy nations where automation concentrates economic power among capital owners.

7. Future Societies Need a “Big State” to Distribute Wealth

As work declines in an automated world, societies must find replacement systems to sustain their citizens. The welfare state we know today will need upgrades. Relying on labor to redistribute wealth won’t work when jobs become scarce.

The author proposes a “Big State,” a government system focused on taxing automation’s winners to support its losers. Beyond simple Universal Basic Income (UBI), the solution could include Conditional Basic Income (CBI), which ties community-focused payments to shared values.

This transition challenges old systems but provides a pathway to foster equality and support in the coming economic landscape.

Examples

• The welfare state emerged in the twentieth century but now struggles as long-term work itself becomes uncertain.
• UBI concepts have gained traction, though critics argue they may feel unfair or indiscriminate.
• CBI, unlike UBI, aims to direct funds to specific populations, addressing community needs without fragmenting societal trust.

8. Technology Boosts Productivity Without Increasing Jobs

One of automation’s paradoxes is that while it expands production and profit, it doesn’t necessarily translate to human employment. Instead, technologies work independently or reduce human workloads.

Major companies, for example, may use machines to scale production while decreasing labor requirements. The resulting economic growth isn’t directly tied to job creation, as it once was during earlier industrial revolutions.

The challenge lies in finding updated systems for measuring success – ones less tied to employment and more focused on societal well-being.

Examples

• Factory robots allow companies to double production while hiring fewer workers.
• Uber-like services invest heavily in driverless cars to fulfill increasing rides without increasing human labor.
• Food delivery innovations such as drone services bypass the need for courier-based employment.

9. Low-Skilled Jobs Shift but Never Fully Disappear

Lower-skill roles, often routine or manual, are hit first by automation. However, certain jobs persist or evolve, based on human needs or inefficiencies in automation. Think caregiving or constructing homes – robots can supplement, but not completely replace human elements.

Rather than disappearing, such roles often move or adapt. They evolve alongside technology, making displacement a temporary rather than permanent phenomenon.

Understanding this pattern helps predict industries most resilient against outright automation while identifying areas for retraining displaced workers.

Examples

• Care work in aging societies continues to rely on human empathy, even with robotic supplements.
• Building homes always requires oversight and adaptation to environmental unpredictability.
• Retail cashier roles adapt into customer-focused or online inventory positions.

Takeaways

1. Master new tools and technologies to enhance your productivity within any field, from basic AI learning to data-handling software.
2. Advocate for community-driven redistribution systems, like Conditional Basic Income, to adapt economies for automation’s impact.
3. Use automation to complement and innovate your work – whether through task simplification, data handling, or creative problem-solving.