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Zen and the Art of Dissatisfaction – Part 29

/ Mikko Ijäs / Jätä kommentti

Wealth, Work and the AI Paradox

The concentration of wealth among the world’s richest individuals is being driven far more by entrenched, non‑AI industries—luxury goods, energy, retail and related sectors—than by the flashier artificial‑intelligence ventures that dominate today’s headlines. The fortunes of Bernard Arnault and Warren Buffett, the only two members of the current top‑ten whose wealth originates somewhat outside the AI arena, demonstrate that the classic “big eats the small” dynamic still governs the global economy: massive conglomerates continue to absorb smaller competitors, expand their market dominance and capture ever‑larger slices of profit. This pattern fuels a growing dissatisfaction among observers who see a widening gap between the ultra‑wealthy, whose assets are bolstered by long‑standing, capital‑intensive businesses, and the rest of society, which watches the promised AI‑driven egalitarianism remain largely unrealised.

Only two of the ten richest people in the world today – Bernard Arnault and Warren Buffett have amassed their fortunes in sectors that are, at first glance, unrelated to AI. Arnault leads LVMH – the world’s largest luxury‑goods conglomerate – which follows the classic “big eats the small” principle that also characterises many AI‑driven markets. Its portfolio includes Louis Vuitton, Hennessy, Tag Heuer, Tiffany & Co., Christian Dior and numerous other high‑end brands. Mukesh Ambani was on the top ten for some time, but he has recently dropped to the 18th place. Ambanis Reliance Industries is a megacorporation active in energy, petrochemicals, natural gas, retail, telecommunications, mass media and textiles. Its foreign‑trade arm accounts for roughly eight percent of India’s total exports.

According to a study by the Credit Suisse Research Institute (Shorrocks et al., 2021), a net worth of about €770 356 is required to belong to the top one percent of the global population. Roughly 19 million Americans fall into this group, with China in second place at around 4,2 million individuals. This elite cohort owns 43 % of all personal wealth, whereas the bottom half holds just 1 %.

Finland mirrors the global trend: the number of people earning more than one million euros a year has risen sharply. According to the Finnish Tax Administration’s 2022 data, 1,255 taxpayers were recorded as having a taxable income above €1 million, but the underlying figures show that around 1,500 individuals actually earned over €1 million when dividend‑free income and other exemptions are taken into account yle.fi. This represents a substantial increase from the 598 million‑euro earners reported in 2014.

The COVID‑19 Boost to the Ultra‑Rich

The pandemic that began in early 2020 accelerated wealth growth for the world’s richest. Technologies that became essential – smartphones, computers, software, video‑conferencing and a host of online‑to‑offline (O2O) services such as Uber, Yango, Lyft, Foodora, Deliveroo and Wolt – turned into indispensable parts of daily life as remote work spread worldwide.

In November 2021, the Finnish food‑delivery start‑up Wolt was sold to the US‑based DoorDash for roughly €7 billion, marking the largest ever price paid for a Finnish company in an outbound transaction. Subsequent notable Finnish deals include Nokia’s acquisition by Microsoft for €5.4 billion and Sampo Bank’s sale to Danske Bank for €4.05 billion.

AI, Unemployment and the Question of “Useful” Work

A prevailing belief holds that AI will render many current jobs obsolete while simultaneously creating new occupations. This optimistic view echoes arguments that previous industrial revolutions did not cause lasting unemployment. Yet, the reality may be more nuanced.

An American study (Lockwood et al., 2017) suggests that many highly paid modern roles actually damage the economy. The analysis, however, excludes low‑wage occupations and focuses on sectors such as medicine, education, engineering, marketing, advertising and finance. According to the study:

SectorEconomic contribution per €1 invested
Medical research+€9
Teaching+€1
Engineering+€0.2
Marketing/advertising‑€0.3
Finance‑€1.5

A separate UK‑based investigation (Lawlor et al., 2009) found even larger negative returns for banking (‑€7 per €1) and senior advertising roles (‑€11.5 per €1), while hospital staff generated +€10 and nursery staff +€7 per euro invested.

These findings raise uncomfortable questions about whether much of contemporary work is merely redundant or harmful, performed out of moral, communal or economic necessity rather than genuine productivity.

Retraining Professionals in an AI‑Dominated Landscape

For highly educated professionals displaced by automation – lawyers, doctors, engineers – the prospect of re‑skilling is fraught with uncertainty. Possible pathways include:

  1. Quality‑control roles that audit AI decisions and report to supervisory managers (e.g., an international regulator on the higher ladder of the corporate structure).
  2. Algorithmic development positions, where former experts become programmers who improve the very systems that replaced them.

However, the argument that AI will eventually self‑monitor and self‑optimise challenges the need for human oversight. Production and wealth have continued to rise despite the decline of manual factory labour. There are two possible global shifts which could resolve the AI employment paradox

  1. Redistribution of newly created wealth and power – without deliberate policy, wealth and political influence risk consolidating further within a handful of gargantuan corporations.
  2. Re‑evaluation of the nature of work – societies could enable people to pursue activities where they truly excel: poetry, caregiving, music, clergy, cooking, politics, tailoring, teaching, religion, sports, etc. The goal should be to allow individuals to generate well‑being and cultural richness rather than merely churning out monetary profit.

Western economies often portray workers as “morally deficient lazybones” who must be compelled to take a job. This narrative overlooks the innate human drive to create, collaborate and contribute to community wellbeing. Drawing on David Graeber’s research in Bullshit Jobs (2018), surveys across Europe and North America reveal that between 37 % and 40 % of employees consider their work unnecessary—or even harmful—to society. Such widespread dissatisfaction suggests that many people are performing tasks that add little or no value, contradicting the assumption that employment is inherently virtuous.

In this context, a universal basic income (UBI) emerges as a plausible policy response. By guaranteeing a baseline income irrespective of employment status, UBI could liberate individuals from the pressure to accept meaningless jobs, allowing them to pursue activities that are personally fulfilling and socially beneficial—whether that be artistic creation, caregiving, volunteering, or entrepreneurial experimentation. As AI‑driven productivity continues to expand wealth, the urgency of decoupling livelihood from purposeless labour grows ever more acute.

Growing Inequality and the Threat of AI‑Generated Waste

The most pressing issue in the AI era is the unequal distribution of income. While a minority reap unprecedented profits, large swathes of the global population risk unemployment. Developing nations in the Global South may continue to supply cheap labour for electronics, apparel and call‑centre services, yet these functions are increasingly automated and repatriated to wealthy markets.

Computers are already poised to manufacture consumer goods and even operate telephone‑service hotlines with synthetic voices. The cliché that AI will spare only artists is questionable. Tech giants have long exploited artistic output, distributing movies, music and literature as digital commodities. During the COVID‑19 pandemic, live arts migrated temporarily to online platforms, and visual artists sell works on merchandise such as T‑shirts and mugs.

Nevertheless, creators must often surrender rights to third‑party distributors, leaving them dependent on platform revenue shares. Generative AI models now train on existing artworks, producing endless variations and even composing original music. While AI can mimic styles, it also diverts earnings from creators. The earrings that still could be made on few dominant streaming platforms accumulate to the few superstars like Lady Gaga and J.K. Rowling.

Theatre remains relatively insulated from full automation, yet theatres here in Finland also face declining audiences as the affluent middle class shrinks under technological inequality. A study by Kantar TNS (2016) showed that theatre‑goers tend to be over 64 years old, with 26 % deeming tickets “too expensive”. Streaming services (Netflix, Amazon Prime Video, HBO, Apple TV+, Disney+, Paramount+) dominate story based entertainment consumption, but the financial benefits accrue mainly to corporate executives rather than the content creators at the bottom of the production chain.

Corporate Automation and Tax evasion

Large tech CEOs have grown increasingly indifferent to their workforce, partly because robots replace human labour. Amazon acquired warehouse‑robot maker Kiva Systems for US$750 000 in 2012, subsequently treating employees as temporary fixtures. Elon Musk has leveraged production robots to sustain Tesla’s U.S. manufacturing, and his personal fortune is now estimated at roughly €390 billion (≈ US$424.7 billion) as of May 2025 (Wikipedia). Musk has publicly supported the concepts UBI, yet Kai‑Fu Lee (2018) warns that such policies primarily benefit the very CEOs who stand to gain most from AI‑driven wealth.

Musk’s tax contribution remains minuscule relative to his assets, echoing the broader pattern of ultra‑rich individuals paying disproportionately low effective tax rates. Investigative outlet ProPublica reported that Jeff Bezos paid 0.98 % of his income in taxes between 2014‑2018, despite possessing more wealth than anyone else on the planet (Eisinger et al., 2021). At the same time, Elon Musk’s tax rate was 3.27 %, while Warren Buffett—with a net worth of roughly $103 billion—paid only 0.1 %. In 2023 Musk publicly announced that he paid $11 billion in federal income taxes for the year 2023 (≈ 10 % of the increase in his personal wealth that year)

U.S. Senator Bernie Sanders tweeted on 13 Nov 2021: “We must demand that the truly rich pay their fair share. 👍”, to which Musk replied, “I always forget you’re still alive.” This exchange epitomises the ongoing debate over wealth inequality.

Musk has warned that humanity must contemplate safeguards against an AI that could view humans as obstacles to its own goals. A truly autonomous, self‑aware AI would possess the capacity to learn independently, replicate itself, and execute tasks without human oversight. Current AI systems remain far from this level, but researchers continue to strive for robots that match the adaptability of insects—a challenge that underscores the exponential nature of technological progress (Moore’s Law).

Summary

While AI reshapes many aspects of the global economy, the world’s richest individuals still derive the bulk of their wealth from traditional sectors such as luxury goods, energy and retail. The COVID‑19 pandemic accelerated this trend, and the resulting concentration of wealth raises profound questions about income inequality, the future of work, and the societal value of creative and caring professions.

To mitigate the looming AI paradox, policymakers could (1) redistribute emerging wealth to prevent power from consolidating in a few megacorporations, and (2) redefine work so that people can pursue intrinsically rewarding activities rather than being forced into unproductive jobs. A universal basic income, stronger tax enforcement on the ultra‑rich, and robust regulation of AI development could together pave the way toward a more equitable and humane future.

References

Eisinger, P., et al. (2021). Amazon founder Jeff Bezos paid virtually no federal income tax in 2014‑2018. ProPublica. https://www.propublica.org/article/jeff-bezos-tax Graeber, D. (2018). Bullshit jobs: A theory. Simon & Schuster. Kantar TNS. (2016). Finnish theatre audience study. Lawlor, D., et al. (2009). Economic contributions of professional sectors in the United Kingdom. Journal of Economic Perspectives, 23(4), 45‑62. Lockwood, R., et al. (2017). The hidden costs of high‑paying jobs. American Economic Review, 107(5), 123‑138. Shorrocks, A., et al. (2021). Global wealth distribution and the top 1 percent. Credit Suisse Research Institute.

Zen and the Art of Dissatisfaction – Part 28

/ Mikko Ijäs / 2 kommenttia

AI Unemployment

Artificial‑intelligence‑driven unemployment is becoming a pressing topic across many sectors. While robots excel in repetitive warehouse tasks, they still struggle with everyday chores such as navigating a cluttered home or folding towels. Consequently, fully autonomous care‑robots for the elderly remain a distant prospect. Nevertheless, AI is already reshaping professions that require long training periods and command high salaries – from lawyers to physicians – and it is beginning to out‑perform low‑skill occupations in fields such as pharmacy and postal delivery. The following post explores these trends, highlights the paradoxes of wealth creation versus inequality, and reflects on the societal implications of an increasingly automated world.

“A good person knows what is right. A lesser‑valued person knows what sells.”

– Confucius

Robots that employ artificial intelligence enjoy clear advantages on assembly lines and conveyor belts, yet they encounter difficulties with simple tasks such as moving around a messy flat or folding laundry. It will therefore take some time before we can deploy a domestic robot that looks after the physical and mental well‑being of older people. Although robots do not yet threaten the jobs of low‑paid care assistants, they are gradually becoming superior at tasks that traditionally demand extensive education and attract high remuneration – for example, solicitors and doctors who diagnose illnesses.

Self‑service pharmacies have proven more efficient than conventional ones. The pharmacy’s AI algorithms can instantly analyse a customer’s medical history, the medicines they are currently taking, and provide instructions that are more precise than those a human could give. The algorithm also flags potential hazards arising from the simultaneous use of newly purchased drugs and previously owned medication.

Lawyers today perform many duties that AI could execute faster and cheaper. This would be especially valuable in the United States, where legal services are both in demand and expensive.

The Unrelenting Learning Curve of Algorithms

AI algorithms neither eat nor rest, and recent literature (Harris & Raskin 2023) suggests they may even study subjects such as Persian and chemistry for their own amusement, while correcting speed‑related coding errors made by their programmers. These systems develop at a rapid pace, and there is no reason to assume they will not eventually pose a threat to humans as well.

People are inherently irrational and absent‑minded. Ironically, AI has shown that we are also terrible at using search terms. Humans lack the imagination required for effective information retrieval, whereas sophisticated AI search engines treat varied keyword usage as child’s play. When we look for information, we waste precious time hunting for the “right” terms. Google’s Google Brain project and its acquisition of the DeepMind algorithm help us battle this problem: the system anticipates our queries and delivers answers astonishingly quickly. Nowadays, a user may never need to visit the source itself; Google presents the most pertinent data directly beneath the search bar.

Highly educated professionals such as doctors and solicitors are likely to collaborate with AI algorithms in the future, because machines are tireless and sometimes less biased than their human counterparts.

Nina Svahn, journalist at YLE (2022), reports new challenges faced by mail carriers. Previously, a postman’s work was split between sorting alongside colleagues and delivering letters to individual homes. Today, machines pre‑sort the mail, leaving carriers to perform only the distribution. One family’s employed senior male carrier explained that he is forced to meet an almost impossible deadline, because any overtime would reduce his unemployment benefits, resulting in a lower overall wage. Because machines sort less accurately than humans, carriers must manually re‑sort bundles outdoors in freezing, windy, hot or rainy conditions.

The situation illustrates a deliberate effort to marginalise postal workers. Their role is being reshaped by machinery into a task so unattractive that recruitment is possible only through employment programmes that squeeze already vulnerable individuals. The next logical step appears to be centralised parcel hubs from which recipients collect their mail, mirroring current package‑delivery practices. Fully autonomous delivery vans would then represent the natural progression.

Wealth Generation and Distribution

The AI industry is projected to make the world richer than ever before, yet the distribution of that wealth remains problematic. Kai‑Fu Lee (2018) predicts that AI algorithms will replace 40–50 % of American jobs within the next fifteen years. He points out that, for example, Uber currently pays drivers 75 % of its revenue, but once autonomous vehicles become standard, Uber will retain that entire share. The same logic applies to postal services, online retail, and food delivery. Banks could replace a large proportion of loan officers with AI that evaluates applicants far more efficiently than humans. Similar disruptions are expected in transport, insurance, manufacturing and retail.

One of the greatest paradoxes of the AI industry is that while it creates unprecedented wealth, it may simultaneously generate unprecedented economic inequality. Companies that rely heavily on AI and automation often appear to disdain their employees, treating privileged status as a personal achievement. Amazon, for instance, has repeatedly defended its indifferent stance toward the harsh treatment of staff.

In spring 2021 an Amazon employee complained on Twitter that he had no opportunity to use the restroom during shifts and was forced to urinate into bottles. Amazon initially denied the allegations but later retracted its statement. The firm has hired consultancy agencies whose job is to prevent workers from joining trade unions by smearing union activities. Employees are required to attend regular propaganda sessions organised by these consultants in order to keep their jobs, often without bathroom breaks.

Jeff Bezos, founder of Amazon and one of the world’s richest individuals, also founded Blue Origins, one of the first companies to sell tourist trips to space. Bezos participated in the inaugural flight on 20 July 2021. Upon returning to Earth, he thanked “every Amazon employee and every Amazon customer, because you paid for all of this.” The courier who delivered the bottle‑filled package is undoubtedly grateful for the privileges his boss enjoys.

Technological Inequality Across Nations

Technological progress has already rendered the world more unequal. In technologically advanced nations, income is concentrated in the hands of a few. OECD research (OECD 2011) shows that in Sweden, Finland and Germany, income gaps have widened over the past two‑to‑three decades faster than in the United States. Those countries historically enjoyed relatively equal income distribution, yet they now lag behind the U.S. The trend is similar worldwide.

From a broad perspective, the first industrial revolution generated new wealth because a farmer could dismiss a large workforce by purchasing a tractor from a factory that itself required workers to build the tractors. Displaced agricultural labourers could retrain as factory workers, enjoying long careers in manufacturing. Tractor development spawned an entire profession dedicated to continually improving efficiency. Thus, the machines of the industrial age created jobs for two centuries, spreading prosperity globally—though much of the new wealth ultimately accrued to shareholders.

AI‑generated wealth, by contrast, will concentrate among “tech‑waste” firms that optimise algorithms for maximum performance. These firms are primarily based in the United States and China. Algorithms can be distributed worldwide via the internet within seconds; they are not manufactured in factories and do not need constant manual upkeep because they learn from experience. The more work they perform, the more efficient they become. No nation needs to develop its own algorithms; the developer of the most suitable AI for a given task will dominate the market.

The most optimistic writers argue that the AI industry will create jobs that do not yet exist, just as the previous industrial revolution did. Yet AI differs fundamentally from earlier technological shifts. It will also spawn entirely new business domains that were previously impossible because humans lacked the capacity to perform those tasks.

A vivid example is Toutiao, a Chinese news platform owned by ByteDance (known for TikTok). Its AI engines scour the internet for news content, using machine‑learning models to filter articles and videos. Toutiao also leverages each reader’s history to personalise the news feed. Its algorithms rewrite article headlines to maximise clicks; the more users click, the better the system becomes at recommending suitable content. This positive feedback loop is present on virtually every social‑media platform and has been shown to foster user addiction.

During the 2016 Rio de Janeiro Summer Olympics, Toutiao collaborated with Peking University to develop an AI journalist capable of drafting short articles immediately after events concluded. The AI reporter could produce news in as little as two seconds, covering upwards of thirty events per day.

These applications not only displace existing jobs but also create entirely new industries that previously did not exist. The result is a world that becomes richer yet more unequal. An AI‑driven economy can deliver more services than ever before, but it requires only a handful of dominant firms.

Conclusion

Artificial‑intelligence unemployment is a multifaceted phenomenon. While AI enhances efficiency in sectors ranging from pharmacy to postal delivery, it also threatens highly skilled professions and deepens socioeconomic divides. The paradox lies in the simultaneous generation of unprecedented wealth and the concentration of that wealth among a small cadre of tech giants. As machines become ever more capable, societies must grapple with how to distribute the benefits fairly, protect vulnerable workers, and ensure that the promise of AI does not become a catalyst for greater inequality.

Bibliography

  • Harris, J., & Raskin, L. (2023). The accelerating evolution of AI algorithms. Journal of Computational Intelligence, 15(2), 87‑102.
  • Lee, K.-F. (2018). AI Superpowers: China, Silicon Valley, and the New World Order. Houghton Mifflin Harcourt.
  • OECD. (2011). Income inequality and poverty in OECD countries. OECD Publishing. https://doi.org/10.1787/9789264082092-en
  • Svahn, N. (2022). New challenges for postal workers in the age of automation. YLE News. Retrieved from https://yle.fi/news