Zen and the Art of Dissatisfaction – Part 29

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 27

From Red Envelopes to Smart Finance

In recent years China has accelerated the intertwining of state‑led surveillance, artificial‑intelligence‑driven finance and ubiquitous digital platforms. The country’s 2017 cyber‑security law introduced harsher penalties for the unlawful collection and sale of personal data, raising the perennial question of how much privacy is appropriate in an era of pervasive digitisation. This post examines the legislative backdrop, the role of pioneering technologists such as Kai‑Fu Lee, the meteoric growth of platforms like WeChat, and the emergence of AI‑powered financial services such as Smart Finance. It also reflects on the broader societal implications of a surveillance‑centric model that is increasingly being mirrored in Western contexts.Subscribe

Originally published in Substack: https://substack.com/home/post/p-172666849

China began enforcing a new cyber‑security law in 2017. The legislation added tougher punishments for the illegal gathering or sale of user data. The central dilemma remains: how much privacy is the right amount in the age of digitalisation? There is no definitive answer to questions about the optimal level of social monitoring needed to balance convenience and safety, nor about the degree of anonymity citizens should enjoy when attending a theatre, dining in a restaurant, or travelling on the metro. Even if we trust current authorities, are we prepared to hand the tools for classification and surveillance over to future rulers?

Kai‑Fu Lee’s Perspective on China’s Data Openness

According to Taiwanese AI pioneer Kai‑Fu Lee (2018), China’s relative openness in collecting data in public spaces gives it a head start in deploying observation‑based AI algorithms. Lee’s background lends weight to his forecasts. His 1988 doctoral dissertation was a groundbreaking work on speech recognition, and from 1990 onward he worked at Apple, Microsoft and Google before becoming a private‑equity investor in 2009. This openness (i.e., the lack of privacy protection) accelerates the digitalisation of urban environments and opens the door to new OMO (online‑merge‑offline) applications in retail, security and transport. Pushing AI into these sectors requires more than cameras and data; creating OMO environments in hospitals, cars and kitchens demands a diverse array of sensor‑enabled hardware to synchronise the physical and digital worlds.

One of China’s most successful companies in recent years has been Tencent, which has been Asia’s most valuable firm since 2016. Its secret sauce is the messaging app WeChat, launched in January 2011 when Tencent already owned two other dominant social‑media platforms. Its QQ instant‑messaging service and Q‑Zone social network each boasted hundreds of millions of users.

WeChat initially allowed users to send photos, short voice recordings and text in Chinese characters, and it was built specifically for smartphones. As the user base grew, its functionalities expanded. By 2013 WeChat had 300 million users; by 2019 that figure rose to 1.15 billion daily active users. It introduced video calls and conference calls several years before the American WhatsApp (today owned by Meta). The app’s success rests on its “app‑within‑an‑app” principle, allowing businesses to create their own mini‑apps inside WeChat—effectively their own dedicated applications. Many firms have abandoned standalone apps and now operate entirely within the WeChat ecosystem.

Over the years, WeChat has captured users’ digital lives beyond smartphones, becoming an Asian “remote control” that governs everyday transactions: paying in restaurants, ordering taxis, renting city bikes, managing investments, booking medical appointments and even ordering prescription medication to the doorstep.

In honour of the Chinese New Year 2014, WeChat introduced digital red envelopes—cash‑filled gifts akin to Western Christmas presents. Users could link their bank accounts to WeChat Pay and send a digital red envelope, with the funds landing directly in the recipient’s WeChat wallet. The campaign prompted five million users to open a digital bank account within WeChat.

Competition from Alipay and the Rise of Cashless Payments

Another Chinese tech titan, Jack Ma, founder of Alibaba, launched the digital payment system Alipay back in 2004. Both Alipay and WeChat enabled users to request payments via simple, printable QR codes as early as 2016. This shift has transformed Chinese phone usage into a primary payment method, to the extent that homeless individuals now beg for money by displaying QR codes. In several Chinese cities cash has effectively disappeared for years.

WeChat and Alipay closely monitor users’ spending habits, building detailed profiles of consumer behaviour. China has largely bypassed a transitional cash‑payment stage: millions moved straight from cash to mobile payments without ever owning a credit card. While both platforms allow users to withdraw cash from linked bank accounts, their core services do not extend credit.

Lee (2018) notes the emergence of a service called Smart Finance, an AI‑powered application that relies solely on algorithms to grant millions of micro‑loans. The algorithm requires only access to the borrower’s phone data, constructing a consumption profile from seemingly trivial signals—such as typing speed, battery level and birthdate—to predict repayment likelihood.

Smart Finance’s AI does not merely assess the amount of money in a WeChat wallet or bank statements; it harvests data points that appear irrelevant to humans. Using these algorithmically derived credit indicators, the system achieves finer granularity than traditional scoring methods. Although the opaque nature of the algorithm prevents public scrutiny, its unconventional metrics have proven highly profitable.

As data volumes swell, these algorithms become ever more refined, allowing firms to extend credit to groups traditionally overlooked by banks—young people, migrant workers, and others. However, the lack of transparency means borrowers cannot improve their scores because the criteria remain hidden, raising fairness concerns.

Surveillance Society: Social Credit and Ethnic Monitoring

Lee reminds us that AI algorithms are reshaping society. From a Western viewpoint, contemporary China resembles a surveillance state where continuous monitoring and a social credit system are routine. Traffic violations can be punished through facial‑recognition algorithms, with fines deducted directly from a user’s WeChat account. WeChat itself tracks users’ movements, language and interactions, acting as a central hub for social eligibility monitoring.

A Guardian article by Johana Bhuiyan (2021) reported that Huaweifiled a July 2018 patent for technology capable of distinguishing whether a person belongs to the Han majority or the persecuted Uyghur minority. State‑contracted Chinese firm Hikvision has developed similar facial‑recognition capabilities for use in re‑education camps and at the entrances of nearly a thousand mosques. China denies allegations of torture and sexual violence against Uyghurs; estimates suggest roughly one million detainees in these camps.

AI‑enabled surveillance is commonplace in China and is gaining traction elsewhere. Amazon offers its facial‑recognition service Rekognition to various clients, although the U.S. police stopped using it in June 2020 amid protests against police racism and violence. Critics highlighted Rekognition’s difficulty correctly identifying gender for darker‑skinned individuals—a claim Amazon disputes.

Google’s image‑search facial‑recognition feature also faced backlash after software engineer Jacky Alciné discovered in 2015 that the system mislabelled African‑American friends as “gorillas.” After public outcry, Google removed the offending categories (gorilla, chimpanzee, ape) from its taxonomy (Vincent 2018).

Limits of Current AI and Future Outlook

Present‑day AI algorithms primarily excel at inference tasks and object detection. General artificial intelligence—capable of autonomous, creative reasoning—remains a distant goal. Nonetheless, we are only beginning to grasp the possibilities and risks of AI‑driven algorithms.

Is the Chinese surveillance model something citizens truly reject? Within China, the social credit system may be viewed positively by ordinary citizens who can boost their scores by paying bills promptly, volunteering and obeying traffic rules. In Europe, a quieter acceptance of similar profiling is emerging: we are already classified—often without our knowledge—through the data we generate while browsing the web. This silent consent fuels targeted advertising for insurance, lingerie, holidays, television programmes and even political persuasion. As long as we are unwilling to pay for the privilege of using social‑media platforms, those platforms will continue exploiting our data as they see fit.

Summary

China’s 2017 cyber‑security law set the stage for an expansive data‑collection regime that underpins a sophisticated surveillance economy. Visionaries like Kai‑Fu Lee highlight how openness in public‑space data fuels AI development, while corporate giants such as Tencent and Alibaba have turned messaging apps into all‑purpose digital wallets and service hubs. AI‑driven financial products like Smart Finance illustrate both the power and opacity of algorithmic credit scoring. Simultaneously, state‑backed facial‑recognition technologies target ethnic minorities, and the social‑credit system normalises continuous monitoring of everyday behaviour. These trends echo beyond China, with Western firms and governments experimenting with comparable surveillance tools. Understanding the interplay between legislation, corporate strategy and AI is essential for navigating the privacy challenges of our increasingly digitised world.


References

Bhuiyan, J. (2021). Huawei files patent to identify UyghursThe Guardian
Lee, K. F. (2018). AI superpowers: China, Silicon Valley, and the new world order. Harper Business. 
Vincent, J. (2018). Google removes offensive labels from image‑search resultsBBC.