Zen and the Art of Dissatisfaction – Part 33

From Poverty to Productivity

Across the world, economists, sociologists and policymakers have long debated whether providing people with an unconditional basic income could help lift them out of poverty. Despite numerous pilot projects, there are relatively few long-term studies showing the large-scale social and health impacts of such measures. One striking exception, highlighted by the Dutch historian Rutger Bregman, provides rare empirical evidence of how a sudden, guaranteed flow of money can transform an entire community — not just economically, but psychologically and socially.

In 1997, in the state of North Carolina, the Eastern Band of Cherokee people opened the Harrah’s Cherokee Casino Resort. By 2010, the casino’s annual revenues had reached around 400 million USD, where they have remained relatively stable ever since. The income was used to build a new school, hospital and fire station — but the most significant portion of the profits went directly to the tribe’s members, about 8,000 in total.

The Findings: Money Really Did Change Everything

By 2001, the funds from the casino already accounted for roughly 25–33 per cent of household income for many families. These payments acted, in effect, as an unconditional basic income.

What made this case extraordinary was that, purely by coincidence, a research group led by psychiatrist Jane Costello at Duke University had been tracking the mental health of young people in the area since 1991. This provided a unique opportunity to compare the same community before and after the introduction of this new source of income.

Costello’s long-term data revealed that children who had grown up in poverty were far more likely to suffer from behavioural problems than their better-off peers. Yet after the casino opened — and the Cherokee families’ financial situation improved — behavioural problems among children lifted out of poverty declined by up to 40 per cent, reaching levels comparable to those of children from non-poor households.

The benefits went beyond behaviour. Youth crime, alcohol consumption and drug use all decreased, while school performance improved significantly. Ten years later, researchers found that the earlier a child had been lifted out of poverty, the better their mental health as a teenager.

Bregman (2018) uses this case to make a clear point: poverty is not caused by laziness, stupidity or lack of discipline. It is caused by not having enough money. When poor families finally have the financial means to meet their basic needs, they frequently become more productive citizens and better parents.

In his words, “Poor people don’t make stupid decisions because they are stupid, but because they live in a context where anyone would make stupid decisions.” Scarcity — whether of time or money — narrows focus and drains cognitive resources, leading to short-sighted, survival-driven choices. And as Bregman puts it poignantly:

“There is one crucial difference between the busy and the poor: you can take a holiday from busyness, but you can’t take a holiday from poverty.”

How Poverty Shapes the Developing Brain

The deeper roots of these findings lie in how poverty and stress affect brain development and emotional regulation. The Canadian physician and trauma expert Gábor Maté (2018) explains how adverse childhood experiences — known as ACE scores — are far more common among children raised in poverty. Such children face a higher risk of being exposed to violence or neglect, or of witnessing domestic conflict in their homes and neighbourhoods.

Chronic stress, insecurity and emotional unavailability of caregivers can leave lasting marks on the developing brain. The orbitofrontal cortex — located behind the eyes and crucial for interpreting non-verbal emotional cues such as tone, facial expressions and pupil size — plays a vital role in social bonding and empathy. If parents are emotionally detached due to stress, trauma or substance use, this brain region may develop abnormally.

Maté describes how infants depend on minute non-verbal signals — changes in the caregiver’s pupils or micro-expressions — to determine whether they are safe and loved. Smiling faces and dilated pupils signal joy and security, whereas flat or constricted expressions convey threat or absence. These signals shape how a child’s emotional circuits wire themselves for life.

When children grow up surrounded by tension or neglect, they may turn instead to peers for validation. Yet peer-based attachment, as Maté notes, often fosters riskier behaviour: substance use, early pregnancy, and susceptibility to peer pressure. Such patterns are not signs of inherent cruelty or weakness, but rather of emotional immaturity born of unmet attachment needs.

Not Just a Poverty Problem: The Role of Emotional Availability

Interestingly, these developmental challenges are not confined to low-income families. Children from wealthy but emotionally absent households often face similar struggles. Parents who are chronically busy or glued to their smartphones may be physically present yet emotionally unavailable. The result can be comparable levels of stress and insecurity in their children.

Thus, whether a parent is financially poor or simply time-poor, the emotional outcome for the child can be strikingly similar. In both cases, high ACE scores predict poorer mental and physical health, lower educational attainment, and reduced social mobility.

While Finland is often praised for its high social mobility, countries like the United States show a much stronger intergenerational persistence of poverty. In rigidly stratified societies, the emotional and economic consequences of childhood disadvantage are far harder to escape.

Towards a More Humane Future: Basic Income and the AI Revolution

As artificial intelligence reshapes industries and redefines the meaning of work, society faces a profound question: how do we ensure everyone has the means — and the mental space — to live well?

If parents could earn their income doing the work they truly value, rather than chasing pay cheques for survival, they would likely become more productive, more fulfilled, and more emotionally attuned to their children. In turn, those children would grow into healthier, happier adults, capable of sustaining positive cycles of wellbeing and productivity.

Such an outcome would not only enhance individual happiness but would also reduce public expenditure on health care, policing and welfare. Investing in people’s emotional and economic stability yields returns that compound across generations. A universal basic income (UBI), far from being utopian, could therefore represent one of the wisest and most humane investments a modern society could make.

Conclusion

The story of the Eastern Band of Cherokee people and the Harrah’s Cherokee Casino stands as powerful evidence that unconditional income can transform lives — not through moral exhortation, but through simple material security. Poverty, as Bregman reminds us, is not a character flaw; it is a cash-flow problem. And as Maté shows, the effects of that scarcity extend deep into the wiring of the human brain. When financial stress eases, parents can connect, children can thrive, and communities can flourish. In an age of automation and abundance, perhaps the greatest challenge is no longer how to produce wealth — but how to distribute it in ways that allow everyone the freedom to be fully human.


References

Bregman, R. (2018). Utopia for Realists: The Case for a Universal Basic Income, Open Borders, and a 15-Hour Workweek. Bloomsbury.
Maté, G. (2018). In the Realm of Hungry Ghosts: Close Encounters with Addiction. North Atlantic Books.

Zen and the Art of Dissatisfaction – 23

Bullshit Jobs and Smart Machines

This post explores how many of today’s high‑paid professions depend on collecting and analysing data, and on decisions made on the basis of that process. Drawing on thinkers such as Hannah ArendtGerd Gigerenzer, and others, I examine the paradoxes of complex versus simple algorithms, the ethical dilemmas arising from algorithmic decision‑making, and how automation threatens not only unskilled but increasingly highly skilled work. I also situate these issues in historical context, from the Fordist assembly line to modern AI’s reach into law and medicine.

Originally published in Substack: https://substack.com/inbox/post/170023572

Many contemporary highly paid professions rely on data gathering, its analysis, and decisions based on that process. According to Hannah Arendt (2017 [original 1963]), such a threat already existed in the 1950s when she wrote:

“The explosive population growth of today has coincided frighteningly with technological progress that makes vast segments of the population unnecessary—indeed superfluous as a workforce—due to automation.”

In the words of David Ferrucci, the leader of Watson’s Jeopardy! team, the next phase in AI’s development will evaluate data and causality in parallel. The way data is currently used will change significantly when algorithms can construct data‑based hypotheses, theories and mental models answering the question “why?”

The paradox of complexity: simple versus black‑box algorithms

Paradoxically, one of the biggest problems with complex algorithms such as Watson and Google Flu Trends is their very complexity. Gerd Gigerenzer (2022) argues that simple, transparent algorithms often outperform complex ones. He criticises secret machine‑learning “black‑box” systems that search vast proprietary datasets for hidden correlations without understanding the physical or psychological principles of the world. Such systems can make bizarre errors—mistaking correlation for causation, for instance between Swiss chocolate consumption and number of Nobel Prize winners, or between drowning deaths in American pools and the number of films starring Nicolas Cage. A stronger correlation exists between the age of Miss America and rates of murder: when Miss America is aged twenty or younger, murders committed by hot steam or weapons are fewer. Gigerenzer advocates for open, simple algorithms; for example, the 1981 model The Keys to the White House, developed by historian Allan Lichtman and geophysicist Vladimir Keilis‑Borok, which has correctly predicted every US presidential election since 1984, with the single exception of the result in the Al Gore vs. George W. Bush contest.

Examples where individuals have received long prison sentences illustrate how secret, proprietary algorithms such as COMPAS (“Correctional Offender Management Profiling for Alternative Sanctions”) produce risk assessments that can label defendants as high‑risk recidivists. Such black‑box systems, which may determine citizens’ liberty, pose enormous risks to individual freedom. Similar hidden algorithms are used in credit scoring and insurance. Citizens are unknowingly categorised and subject to prejudices that constrain their opportunities in society.

The industrial revolution, automation, and the meaning of work

Even if transformative technologies like Watson may fail to deliver on all the bold promises made by IBM’s marketing, algorithms are steadily doing tasks once carried out by humans. Just as industrial machines displaced heavy manual labour and beasts of burden—especially in agriculture—today’s algorithms are increasingly supplanting cognitive roles.

Since the Great Depression of the 1930s, warnings have circulated that automation would render millions unemployed. British economist John Maynard Keynes (1883–1946) coined the term “technological unemployment” to describe this risk. As David Graeber (2018) notes, automation did indeed trigger mass unemployment. Political forces on both the right and left share a deep belief that paid employment is essential for moral citizenship; they agree that unemployment in wealthy countries should never exceed around 8 percent. Graeber nonetheless argues that the Great Depression produced a collapse in real need for work—and much contemporary work is “bullshit jobs”. If 37–40 percent of jobs are such meaningless roles, more than 50–60 percent of the population are effectively unemployed.

Karl Marx warned of industrial alienation, where people are uprooted from their villages and placed into factories or mines to do simple, repetitive work requiring no skill, knowledge or training, and easily replaceable. Global corporations have shifted assembly lines and mines to places where workers have few rights, as seen in electronics assembly in Chinese factory towns, garment workshops in Bangladesh, and mineral extraction by enslaved children—all under appalling conditions.

Henry Ford’s Western egalitarian idea of the assembly line—that all workers are equal—became a system where anybody can be replaced. In Charles Chaplin’s 1936 film Modern Times, inspired by his encounter in 1931 with Mahatma Gandhi, he highlighted our dependence on machines. Gandhi argued that Britain had enslaved Indians through its machines; he sought non‑violent resistance and self‑sufficiency to show that Indians did not need British machines or Britain itself.

From industrial jobs to algorithmic threat to professional work

At its origin in Ford’s factory in 1913, the T‑model moved through 45 fixed stations and was completed in 93 minutes, borrowing the idea from Chicago slaughterhouses where carcasses moved past stationary cutters. Though just 8 percent of the American workforce was engaged in manufacturing by the 1940s, automation created jobs in transport, repair, and administration—though these often required only low-skilled labour.

Today, AI algorithms threaten not only blue‑collar but also white‑collar roles. Professions requiring long training—lawyers and doctors, for example—are now at risk. AI systems can assess precedent for legal cases more accurately than humans. While such systems promise reliability, they also bring profound ethical risks. Human judges are fallible: one Israeli study suggested that judges issue harsher sentences before lunch than after—but that finding has been contested due to case‑severity ordering. Yet such results are still invoked to support AI’s superiority.

Summary

This blog post has considered how our economy is increasingly structured around data collection, analysis, and decision‑making by both complex and simple algorithms. It has explored the paradox that simple, transparent systems can outperform opaque ones, and highlighted the grave risks posed by black‑box algorithms in criminal justice and financial systems. Tracing the legacy from Fordist automation to modern AI, I have outlined the existential threats posed to human work and purpose—not only for low‑skilled labour but for highly skilled professions. The text argues that while automation may deliver productivity, it also risks alienation, injustice, and meaninglessness unless we critically examine the design, application, and social framing of these systems.


References

Arendt, H. (2017). The Human Condition (Original work published 1963). University of Chicago Press.
Ferrucci, D. (n.d.). [Various works on IBM Watson]. IBM Research.
Gigerenzer, G. (2022). How to Stay Smart in a Smart World: Why Human Intelligence Still Beats Algorithms. MIT Press.
Graeber, D. (2018). Bullshit Jobs: A Theory. Simon & Schuster.
Keynes, J. M. (1930). Economic Possibilities for our Grandchildren. Macmillan.
Lee, C. J. (2018). The misinterpretation of the Israeli parole study. Nature Human Behaviour, 2(5), 303–304.
Lichtman, A., & Keilis-Borok, V. (1981). The Keys to the White House. Rowman & Littlefield.