Kirjoittaja: Mikko Ijäs

Zen and the Art of Dissatisfaction – Part 27

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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 Uyghurs. The 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 results. BBC.

Zen and the Art of Dissatisfaction – Part 26

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Unrelenting Battle for AI Supremacy

In today’s fast-evolving digital landscape, the titanic technology corporations are locked in a merciless struggle for AI dominance. Their competitive advantage is fuelled by the ability to access vast quantities of data. Yet this race holds profound implications for privacy, ethics, and the overlooked human labour that quietly powers it.

Originally published in Substack: https://substack.com/home/post/p-172413535

Large technology conglomerates are engaged in a cutthroat contest for AI supremacy, a competition shaped in large part by the free availability of data. Chinese rivals may be narrowing the gap in this contest, where the free flow of data reigns supreme. In contrast, in Western nations, personal data remains, at least for now, considered the property of the individual; its use requires the individual’s awareness and consent. Nevertheless, people freely share their data—opinions, consumption habits, images, location—when signing up for platforms or interacting online. The freer companies can exploit this user data, the quicker their AI systems learn. Machine learning is often applauded because it promises better services and more accurately targeted advertisements.

Hidden Human Labour

Yet, behind these learning systems are human workers—micro‑workers—who teach data to AI algorithms. Often subcontracted by the tech giants, they are paid meagrely yet exposed to humanity’s darkest content, and they must keep what they see secret. In principle, anyone can post almost anything on social media. Platforms may block or “lock” content that violates their policies—only to have the original poster appeal, rerouting the content to micro‑workers for review.

These shadow workers toil from home, performing tasks such as identifying forbidden sexual content, violence, or categorising products for companies like Walmart and Amazon. For example, they may have to distinguish whether two similar items are the same or retag products into different categories. Despite the rise of advanced AI, these micro‑tasks remain foundational—and are compensated only by the cent.

The relentless gathering of data is crucial for deep‑learning AI systems. In the United States, the tension between user privacy and corporate surveillance remains unresolved—largely stemming from the Facebook–Cambridge Analytica scandal. In autumn 2021, Frances Haugen, a data scientist and whistleblower, exposed how Facebook prioritised maximising user time on the platform over public safety Wikipedia+1.

Meanwhile, the roots of persuasive design trace back to Stanford University’s Persuasive Technology Lab (now known as the Behavior Design Lab), under founder B. J. Fogg, where concepts to hook and retain users—regardless of the consequences—were born. On face value, social media seems benign—connecting people, facilitating ideas, promoting second‑hand sales. Yet beneath the surface lie algorithms designed to keep users engaged, often by feeding content tailored to their interests. The more platforms learn, the more they serve users exactly what they want—drawing them deeper into addictive cycles.

Renowned psychologists from a PNAS study found that algorithms—based on just a few likes—could know users better than even their closest friends. About 90 likes enabled better personality predictions than an average friend, while 270 likes made AI more accurate than a spouse.

The Cambridge Analytica scandal revealed how personal data can be weaponised to influence political outcomes in events like Brexit and the 2016 US Presidential Election. All that was needed was to identify and target individuals with undecided votes based on their location and psychological profiles.

Frances Haugen’s whistleblowing further confirmed that Facebook exacerbates political hostility and supports authoritarian messaging especially in countries like Brazil, Hungary, the Philippines, India, Sri Lanka, Myanmar, and the USA.

As critics note, these platforms never intended to serve as central political channels—they were optimized to maximise engagement and advertising revenue. One research group led by Laura Edelson found that misinformation posts received six times more likes than posts from trusted sources like CNN or the World Health Organization The Guardian.

In theory, platforms could offer news feeds filled exclusively with content that made users feel confident, loved, safe—but such feeds don’t hold attention long enough for profit. Instead, platforms profit more from cultivating anxiety, insecurity, and outrage. The algorithm knows us so deeply that we often don’t even realise when we’re entirely consumed by our feelings, fighting unseen ideological battles. Hence, ad-based revenue models prove extremely harmful. Providers could instead charge a few euros a month—but the real drive is harvesting user data for long‑term strategic advantage.

Conclusion

The race for AI supremacy is not just a competition of algorithms—it’s a battle over data, attention, design, and ethics. The tech giants are playing with our sense of dissatisfasction, and we have no psychological tools to avoid it. While tech giants vie for the edge, a hidden workforce labours in obscurity, and persuasive systems steer human behaviour toward addiction and division. Awareness, regulation, and ethical models—potentially subscription‑based or artist‑friendly—are needed to reshape the future of AI for human benefit.

References

B. J. Fogg. (n.d.). B. J. Fogg. Wikipedia. Retrieved from https://en.wikipedia.org/wiki/B._J._Fogg
Behavior Design Lab. (n.d.). Stanford Behavior Design Lab. Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Stanford_Behavior_Design_Lab
Captology. (n.d.). Captology. Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Captology
Frances Haugen. (n.d.). Frances Haugen. Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Frances_Haugen
2021 Facebook leak. (n.d.). 2021 Facebook leak. Wikipedia. Retrieved from https://en.wikipedia.org/wiki/2021_Facebook_leak

Zen and the Art of Dissatisfaction – Part 25

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Exponential Futures

Throughout history, humanity has navigated the interplay between population growth, technological progress, and ethical responsibility. As automation, artificial intelligence, and biotechnology advance at exponential rates, philosophers, scientists, and entrepreneurs have raised profound questions: Are we heading towards liberation from biological limits, or into a new form of dependency on machines? Can we satisfy our dissatisfaction with more intelligent machines and unlimited growth? What would be enough? The following post explores these dilemmas, drawing from historical parables, the logic of Moore’s law, transhumanism, and the latest breakthroughs in artificial intelligence.

“The current explosive growth in population has frighteningly coincided with the development of technology, which, due to automation, makes large parts of the population ‘superfluous’, even as labour. Because of nuclear energy, this double threat can be tackled with means beside which Hitler’s gas chambers look like the malicious child’s play of an evil brat.”
– Hannah Arendt

Originally published in Substack: https://substack.com/inbox/post/171630771

Our technological development has been tied to Moore’s law. Named after Gordon Moore, the founder of Intel, one of the world’s largest semiconductor manufacturers, the law states that the number of transistors on a microchip doubles every 18–24 months. As a result, chips become more powerful while their price falls. Moore’s prediction in 1965 has remained remarkably accurate, as innovation has kept the process alive long past the point when the laws of physics should have slowed it down. This type of growth is called exponential, characterised by slow initial development which suddenly accelerates at an unexpected pace.

A Parable of Exponential Growth

The Islamic scholar Ibn Khallikan described the logic of exponential growth in a tale from 1256. According to the story, chess originated in India during the 6th century. Its inventor travelled to Pataliputra and presented the game to the emperor. Impressed, the ruler offered him any reward. The inventor requested rice, calculated using the chessboard: one grain on the first square, two on the second, four on the third, doubling with each square.

Such exponential growth seems modest at first, but by the 64th square it yields more than 18 quintillion grains of rice, or about 1.4 trillion tonnes. By comparison, the world currently produces about 772 million tonnes of wheat annually. The inventor’s demand thus exceeded yearly wheat production by a factor of over 2,000. The crucial lesson lies not in the quantity but in the speed at which exponential processes accelerate.

The central question remains: at what stage of the chessboard are we today in terms of microchip development? According to Moore’s law, we are heading towards an increasingly technological future. Futurists such as Ray Kurzweil, Chief Engineer at Google, believe that transhumanism is the only viable path for humanity to collaborate with AI. Kurzweil predicts that artificial intelligence will surpass human mental capabilities by 2045.

Transhumanism posits that the limits of the human biological body are a matter of choice. For transhumanists, ageing should be voluntary, and cognitive capacities should lie within individual control. Kurzweil forecasts that by 2035 nanobots will be implanted in our brains to connect with neurons, upgrading both mental and physical abilities. The aim is to prevent humans from becoming inferior to machines, preserving self-determination.

The Intelligence of Machines – Real or Illusion?

Yet artificial intelligence has not, until recently, been very intelligent. Algorithms can process data and make deductions, but image recognition, for example, has long struggled with tasks a child could solve instantly. A child, even after seeing a school bus once, can intuitively identify it; an algorithm, trained on millions of images, may still fail under slightly altered conditions. This gap between human intuition and machine logic underscores the challenge.

Nevertheless, AI is evolving rapidly. Vast financial resources drive competition over the future of intelligence and power.

The South African-born Elon Musk, founder of Neuralink, has already demonstrated an implant that allows a monkey named Pager to play video games using only thought. Musk suggests such implants could treat depression, Alzheimer’s disease, and paralysis, and even restore sight to the blind.

Though such ideas may sound outlandish, history suggests that visionary predictions often materialise sooner than expected.

The Warnings of Tristan Harris

Tristan Harris, who leads the non-profit Centre for Humane Technology, has been at the heart of Silicon Valley’s AI story, from Apple internships to Instagram development and work at Google. In 2023, alongside Aza Raskin, he warned of AI’s dangers. Their presentation demonstrated AI systems capable of cloning a human voice within seconds, or reconstructing mental images using fMRI brain scans.

AI models have begun to exhibit unexpected abilities. A system trained in English suddenly understands Persian. ChatGPT, launched by OpenAI, has independently learned advanced chemistry, though it was never explicitly trained in the subject. Algorithms now self-improve, rewriting code to double its speed, creating new training data, and exhibiting exponential capability growth. Experts foresee improvements at double-exponential rates, represented on a graph as a near-vertical line surging upwards.

Conclusion

The trajectory of human civilisation now intertwines with exponential technological growth. From the rice-on-the-chessboard parable to Moore’s law and the visions of Kurzweil, Musk, and Harris, the central issue remains: will humanity adapt, or will machines redefine what it means to be human? The pace of change is no longer linear, and as history shows, exponential processes accelerate suddenly, leaving little time to adjust.

References

Arendt, H. (1963). Eichmann in Jerusalem: A report on the banality of evil. Viking Press.
Harris, T., & Raskin, A. (2023). The AI dilemma [Presentation]. Center for Humane Technology.
Kurzweil, R. (2005). The singularity is near: When humans transcend biology. Viking.
Moore, G. E. (1965). Cramming more components onto integrated circuits. Electronics, 38(8).

Zen and the Art of Dissatisfaction – Part 24

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How Algorithms and Automation Redefine Work and Society

The concept of work in Western societies has undergone dramatic transformations, yet in some ways it has remained surprisingly static. Work and the money made with work also remains one of the leading causes for dissatisfactoriness. There’s usually too much work and the compensation never seems to be quite enough. While the Industrial Revolution replaced manual labour with machinery, the age of Artificial Intelligence (AI) threatens to disrupt not only blue-collar jobs but also highly skilled professions. This post traces the historical shifts in the nature of work, from community-driven agricultural labour to the rise of mass production, the algorithmic revolution, and the looming spectre of general artificial intelligence. Along the way, it examines the ethical, economic, and social implications of automation, surveillance, and machine decision-making — raising critical questions about the place of humans in a world increasingly run by machines.

Originally published in Substack: https://substack.com/home/post/p-170864875

The Western concept of work has hardly changed in essence: half the population still shuffles papers, projecting an image of busyness. The Industrial Revolution transformed the value of individual human skill, rendering many artisanal professions obsolete. A handcrafted product became far more expensive compared to its mass-produced equivalent. This shift also eroded the communal nature of work. Rural villagers once gathered for annual harvest festivities, finding strength in togetherness. The advent of threshing machines, tractors, and milking machines eliminated the need for such collective efforts.

In his wonderful and still very important film Modern Times (1936), Charlie Chaplin depicts industrial society’s alienating coexistence: even when workers are physically together, they are often each other’s competitors. In a factory, everyone knows that anyone can be replaced — if not by another worker, then by a machine.

In the early 1940s, nearly 40% of the American workforce was employed in manufacturing; today, production facilities employ only about 8%. While agricultural machinery displaced many farmworkers, those machines still require transportation, repairs, and eventual replacement — generating jobs in other, less specialised sectors.

The Algorithmic Disruption

Artificial intelligence algorithms have already displaced workers in multiple industries, but the most significant disruption is still to come. Previously, jobs were lost in sectors requiring minimal training and were easily passed on to other workers. AI will increasingly target professions demanding long academic training — such as lawyers and doctors. Algorithms can assess legal precedents for future court cases more efficiently than humans, although such capabilities raise profound ethical issues.

One famous Israeli study suggested that judges imposed harsher sentences before lunch than after (Lee, 2018). Although later challenged — since case order was pre-arranged by severity — it remains widely cited to argue for AI’s supposed superiority in legal decision-making.

Few domains reveal human irrationality as starkly as traffic. People make poor decisions when tired, angry, intoxicated, or distracted while driving. In 2016, road traffic accidents claimed 1.35 million lives worldwide. In Finland in 2017, 238 people died and 409 were seriously injured in traffic; there were 4,432 accidents involving personal injury.

The hope of the AI industry is that self-driving cars will vastly improve road safety. However, fully autonomous vehicles remain distant, partly because they require a stable and predictable environment — something rare in the real world. Like all AI systems, they base predictions on past events, which limits their adaptability in chaotic, unpredictable situations.

Four Waves of Machine-Driven Change

The impact of machines on human work can be viewed as four distinct waves:

  1. The Industrial Revolution — people moved from rural to urban areas for factory jobs.
  2. The Algorithmic Wave — AI has increased efficiency in many industries, with tech giants like Amazon, Apple, Alphabet, Microsoft, Huawei, Meta Platforms, Alibaba, IBM, Tencent, and OpenAI leading the way. In 2020, their combined earnings were just under USD 1.5 trillion. Today they are pushing 2 trillion. The leader, Amazon, making 630 billion dollars per year. 
  3. The Sensorimotor Machine Era — autonomous cars, drones, and increasingly automated factories threaten remaining manual jobs.
  4. The Age of Artificial General Intelligence (AGI) — as defined by Nick Bostrom (2015), machines could one day surpass human intelligence entirely.

The rise of AI-driven surveillance evokes George Orwell’s Nineteen Eighty-Four (1949), in which people live under constant watch. Modern citizens voluntarily buy devices that track them, competing for public attention online. Privacy debates date back to the introduction of the Kodak camera in 1888 and intensified in the 1960s with computerised tax records. Today, exponentially growing data threatens individual privacy in unprecedented ways.

AI also inherits human prejudices. Studies show that people with African-American names face discrimination from algorithms, and biased data can lead to unequal treatment based on ethnicity, gender, or geography — reinforcing, rather than eliminating, inequality.

Conclusion

From the threshing machine to the neural network, every technological leap has reshaped the world of work, altering not only what we do but how we define ourselves. The coming decades may bring the final convergence of machine intelligence and autonomy, challenging the very premise of human indispensability. The question is not whether AI will change our lives, but how — and whether we will have the foresight to ensure that these changes serve humanity’s best interests rather than eroding them.

References

Bostrom, N. (2015). Superintelligence: Paths, dangers, strategies. Oxford University Press.
Lee, D. (2018). Do you get fairer sentences after lunch? BBC Future.
Orwell, G. (1999). Nineteen eighty-four. Penguin. (Original work published 1949)

Zen and the Art of Dissatisfaction – 23

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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 Arendt, Gerd 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.

Zen and the Art of Dissatisfaction  – Part 22

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Big Data, Deep Context

In this post, we explore what artificial intelligence (AI) algorithms, or rather – large language models – are, how they learn, and their growing impact on sectors such as medicine, marketing and digital infrastructure. We look into some prominent real‑world examples from the recent past—IBM’s Watson, Google Flu Trends, and the Hadoop ecosystem—and discuss how human involvement remains vital even as machine learning accelerates. Finally, we reflect on both the promise and the risks of entrusting complex decision‑making to algorithms.

Originally published in Substack: https://substack.com/inbox/post/168617753

Artificial intelligence algorithms function by ingesting training data, which guides their learning. How this data is acquired and labelled marks the key differences between various types of AI algorithms. An AI algorithm receives training data and uses it to learn. Once trained, the algorithm performs new tasks using that data as the basis for its future decisions.

AI in Healthcare: From Watson to Robot Doctors

Some algorithms are capable of learning autonomously, continuously integrating new information to adjust and refine their future actions. Others require a programmer’s intervention from time to time. AI algorithms fall into three main categories: supervised learning, unsupervised learning and reinforcement learning. The primary differences between these approaches lie in how they are trained and how they operate.

Algorithms learn to identify patterns in data streams and make assumptions about correct and incorrect choices. They become more effective and accurate the more data they receive—a process known as deep learning, based on artificial neural networks that distinguish between right and wrong answers, enabling them to draw better and faster conclusions. Deep learning is widely used in speech, image and text recognition and processing.

Modern AI and machine learning algorithms have empowered practitioners to notice things they might otherwise have missed. Herbert Chase, a professor of clinical medicine at Columbia University in New York, observed that doctors sometimes have to rely on luck to uncover underlying issues in a patient’s symptoms. Chase served as a medical adviser to IBM during the development of Watson, the AI diagnostic assistant.

IBM’s concept involved a doctor inputting, for example, three patient‑described symptoms into Watson; the diagnostic assistant would then suggest a list of possible diagnoses, ranked from most to least likely. Despite the impressive hype surrounding Watson, it proved inadequate at diagnosing actual patients. IBM therefore announced that Watson would be phased out by the end of 2023 and its clients encouraged to transition to its newer services.

One genuine advantage of AI lies in the absence of a dopamine response. A human doctor, operating via biological algorithms, experiences a rush of dopamine when they arrive at what feels like a correct diagnosis—but that diagnosis can be wrong. When doubts arise, the dopamine fades and frustration sets in. In discouragement, the doctor may choose a plausible but uncertain diagnosis and send the patient home.

An AI‑algorithm‑based “robot‑doctor” does not experience dopamine. All of its hypotheses are treated equally. A robot‑doctor would be just as enthused about a novel idea as about its billionth suggestion. It is likely that doctors will initially work alongside AI‑based robot doctors. The human doctor can review AI‑generated possibilities and make their own judgement. But how long will it be before human doctors become obsolete?

AI in Action: Data, Marketing, and Everyday Decisions

Currently, AI algorithms trained on large datasets drive actions and decision‑making across multiple fields. Robot‑doctors assisting human physicians and the self‑driving cars under development by Google or Tesla are two visible examples of near‑future possibilities—assuming the corporate marketing stays honest.

AI continues to evolve. Targeted online marketing, driven by social media data, is an example of a seemingly trivial yet powerful application that contributes to algorithmic improvement. Users may tolerate mismatched adverts on Facebook, but may become upset if a robot‑doctor recommends an incorrect, potentially expensive or risky test. The outcome is all about data—its quantity, how it is evaluated and whether quantity outweighs quality.

According to MIT economists Erik Brynjolfsson and Andrew McAfee (2014), in the 1990s only about one‑fifth of a company’s activities left a digital trace. Today, almost all corporate activities are digitised, and companies have begun to produce reports in language intelligible to algorithms. It is now more important that a company’s operations are understood by AI algorithms than by its human employees.

Nevertheless, vast amounts of data are still analysed using tools built by humans. Facebook is perhaps the most well‑known example of how our personal data is structured, collected, analysed and used to influence and manipulate opinions and behaviour.

Big Data Infrastructure

Jeff Hammerbacher—in a 2015 interview with Steve Lohr—helped introduce Hadoop in 2008 to manage the ever‑growing volume of data. Hadoop, developed by Mike Cafarella and Doug Cutting, is an open‑source variant of Google’s own distributed computing system. Initially named after Cutting’s child’s yellow toy elephant, Hadoop could process two terabits of data in two days. Two years later it could perform the same task in mere minutes.

At Facebook, Hammerbacher and his team constructed Hive, an application running on Hadoop. Now available as Apache Hive, it allows users without a computer science degree to query large processed datasets. During the writing of this post, generative AI applications such as ChatGPT (by OpenAI), Claude (Anthropic), Gemini (Google DeepMind), Mistral & Mixtral (Mistral AI), and LLaMA (Meta) have become available for casual users on ordinary computers.

A widely cited example of public‑benefit predictive data analysis is Google Flu Trends (GFT). Launched in 2008, GFT aimed to predict flu outbreaks faster than official healthcare systems by analysing popular Google search terms related to flu.

GFT successfully detected the H1N1 virus before official bodies in 2009, marking a major achievement. However, in the winter of 2012–2013, media coverage of flu induced a massive spike in related searches, causing GFT’s estimates to be almost twice the real figures. The Science article “The Parable of Google Flu” (Lazer et al., 2014) accused Google of “big‑data hubris”, although it conceded that GFT was never intended as a standalone forecasting tool, but rather as a supplementary warning signal (Raising the bar, Wikipedia).

Google’s miscalculation lay in its failure to interpret context. Steve Lohr (2015) emphasises that context involves understanding associations—a shift from raw data to meaningful information. IBM’s Watson was touted as capable of such contextual understanding, capable of linking words to appropriate contexts .

Watson: From TV champion to Clinical Tool, and sold for scraps!

David Ferrucci, a leading AI researcher at IBM, headed the DeepQA team responsible for Watson . Named after IBM’s founder Thomas J. Watson, Watson gained prominence after winning £1 million on Jeopardy! in 2011, defeating champions Brad Rutter and Ken Jennings.

Jennifer Chu‑Carroll, one of Watson’s Jeopardy! coaches, told Steve Lohr (2015) that Watson sometimes made comical errors. When asked “Who was the first female astronaut?”, Watson repeatedly answered “Wonder Woman,” failing to distinguish between fiction and reality.

Ken Jennings reflected that:

“Just as manufacturing jobs were removed in the 20th century by assembly‑line robots, Brad and I were among the first knowledge‑industry workers laid off by the new generation of ‘thinking’ machines… The Jeopardy! contestant profession may be the first Watson‑displaced profession, but I’m sure it won’t be the last.”

In February 2013, IBM announced that Watson’s first commercial application would focus on lung cancer treatment and other medical diagnoses—a real‑world “Dr Watson”—with 90% of oncology nurses reportedly following its recommendations at the time. The venture ultimately collapsed under the weight of unmet expectations and financial losses. In January 2022, IBM quietly sold the core assets of Watson Health to private equity firm Francisco Partners—reportedly for about $1 billion, a fraction of the estimated $4 billion it had invested—effectively signalling the death knell of its healthcare ambitions. The sale marked the end of Watson’s chapter as a medical innovator; the remaining assets were later rebranded under the name Merative, a standalone company focusing on data and analytics rather than AI‑powered diagnosis. Slate described the move as “sold for scraps,” characterising the downfall as a cautionary tale of over‑hyped technology failing to deliver on bold promises in complex fields like oncology.

Conclusion

Artificial intelligence algorithms are evolving rapidly, and while they offer significant benefits in fields like medicine, marketing, and data analysis, they also bring challenges. Data is not neutral: volume must be balanced with quality and contextual understanding. Tools such as Watson, Hadoop and Google Flu Trends underscore that human oversight remains indispensable. Ultimately, AI should augment human decision‑making rather than replace it—at least for now.

References

Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.

Ferrucci, D. A., Brown, E., Chu‑Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., … Welty, C. (2011). Building Watson: An overview of the DeepQA project. AI Magazine, 31(3), 59–79. (IBM Research)

Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: traps in big data analysis. Science, 343(6176), 1203–1205. (Wikipedia)

Lohr, S. (2015). Data‑ism. HarperBusiness.

Mintz‑Oron, O. (2010). Smart Machines: IBM’s Watson and the Era of Cognitive Computing. Columbia Business School Publishing. [Referenced via IBM Watson bibliography] (TIME, Wikipedia)

Zen and the Art of Dissatisfaction – Part 21

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Data: The Oil of the Digital Age

Data applications rely fundamentally on data—its extraction, collection, storage, interpretation, and monetisation—making them arguably the most significant feature of our contemporary world. Often referred to as ”the new oil,” data is, from the perspective of persistent capitalists, a valuable resource capable of sustaining economic growth even after conventional natural reserves have been exhausted. This new form of capitalism has been titled Surveillance Capitalism (Zuboff 2019).

Originally published in Substack: https://substack.com/@mikkoijas

Data matters more than opinions. For developers of data applications, the key goal is that we browse online, click “like,” follow links, spend time on their platforms, and accept cookies. What we think or do does not matter; what matters is the digital behavioural surplus, a trace we leave and our consent to tracking. That footprint has become immensely valuable—companies are willing to pay for it, and sometimes break laws to get it.

Cookies and Consumer Privacy in Europe

European legislation like the General Data Protection Regulation (GDPR) ensures some personal protection, but we still leave traces even if we refuse to share personal data. Websites are legally obligated to request our cookie consent, making privacy violations more visible. Rejecting cookies and clearing them out later becomes a time-consuming and frustrating chore.

In stark contrast, China’s data laws are much more relaxed, granting companies broader operational freedom. The more data a company gathers, the more fine-tuned its predictive algorithms can be. It’s much like environmental regulation: European firms are restricted from drilling for oil in protected areas, which reduces profit but protects nature. Chinese firms, unrestrained by such limits, may harm ecosystems while driving profits. In the data realm, restrictive laws narrow the available datasets. Whereas Chinese firms harvest freely, they might gain a major competitive edge that could help them lead the global AI market.

Data for Good: Jeff Hammerbacher’s Vision

American data scientist Jeff Hammerbacher is one of the field’s most influential figures. As journalist Steve Lohr (2015) reports, Hammerbacher started on Wall Street and later helped build Facebook’s data infrastructure. Today, he curates data collection and interpretation for the purpose of improving human lives—a fundamental ethos across the data industry. According to Hammerbacher, we must understand the current data landscape to predict the future. Practically, this means equipping everything we care about with sensors that collect data. His current focus? Transforming medicine by centring it on data. Data science is one of the most promising fields, where evidence trumps intuition.

Hammerbacher has been particularly interested in mental health and how data can improve psychological wellbeing. His close friend and former classmate, Steven Snyder, tragically died by suicide after struggling with bipolar disorder. This event, combined with Hammerbacher’s own breakdown at age 27—after being diagnosed with bipolar disorder and generalised anxiety disorder—led him to rethink his life. He notes that mental illness is a major cause of workforce dropout and ranks third among causes of early death. Researchers are now collecting neurobiological data from those with mental health conditions. Hammerbacher calls this “one of the most necessary and challenging data problems of our time.”

Pharmaceuticals haven’t solved the issue. Selective serotonin reuptake inhibitors(SSRIs), introduced in the 1980s, have failed to deliver a breakthrough for mood disorders. These remain a leading cause of death; roughly 90% of suicides involve untreated or poorly treated mood disorders, and about 50% of Western populations are affected at some point. The greater challenge lies in defining mental wellness—should people simply adapt to lives that feel unfit?

“Bullshit Jobs” and Social Systems

Investigative anthropologist David Graeber (2018) reported that 37–40% of Western workers view their jobs as “bullshit”—work they see as socially pointless. Thus, the problem isn’t merely psychological; our entire social structure normalises employment that values output over wellbeing.

Data should guide smarter decisions. Yet as our world digitises, data accumulates faster than our ability to interpret it. As Steve Lohr (2015) notes, a 20-bed intensive care unit can generate around 160,000 data points per second—a torrent demanding constant vigilance. Still, this data deluge offers positive outcomes: continuous patient monitoring enables proactive, personalised care.

Data-driven forecasting is set to reshape society, concentrating power and wealth. Not long ago, anyone could found a company; now a single corporation could dominate an entire sector with superior data. A case in point is the partnership between McKesson and IBM. In 2009, Kaan Katircioglu (IBM researcher) sought data for predictive modelling. He found it at McKesson—clean datasets recording medication inventory, prices, and logistics. IBM used this to build a predictive model, enabling McKesson to optimise its warehouse near Memphis and improve delivery accuracy from 90% to 99%.

At present, data-mining algorithms behave as clever tools. An algorithm is simply a set of steps for solving problems—think cooking recipes or coffee machine programming. Even novices can produce impressive outcomes by following a good set of instructions.

Historian Yuval Noah Harari (2015) provocatively suggests we are ourselves algorithms. Unlike machines, our algorithms run through emotions, perceptions, and thoughts—biological processes shaped by evolution, environment, and culture.

Summary

Personal data is the new source of extraction and exploitation—vital for technological progress yet governed by uneven regulations that determine competitive advantage. Pioneers like Jeff Hammerbacher highlight its potential for social good, especially in mental health, while revealing our complex psychology. We collect data abundantly, yet face the challenge of interpreting it effectively. Predictive systems can drive efficiency, but they can also foster monopolies. Ultimately, whether data serves or subsumes us depends on navigating its ethical, legal, and societal implications.

References

Graeber, D. (2018). Bullshit Jobs: A Theory. New York: Simon & Schuster.
Hammerbacher, J. (n.d.). [Interview in Lohr 2015].
Harari, Y. N. (2015). Homo Deus: A History of Tomorrow. New York: Harper.
Lohr, S. (2015). Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else. New York: Harper Business.
Zuboff, Shoshana (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.

Zen and the Art of Dissatisfaction – Part 20

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The Triple Crisis of Civilisation

“At the time I climbed the mountain or crossed the river, I existed, and the time should exist with me. Since I exist, the time should not pass away. […] The ‘three heads and eight arms’ pass as my ‘sometimes’; they seem to be over there, but they are now.”

— Dōgen

Introduction

This blog post explores the intertwining of ecology, technology, politics and data collection through the lens of modern civilisation’s crises. It begins with a quote by the Japanese Zen master Dōgen, drawing attention to the temporal nature of human existence. From climate emergency to digital surveillance, from Brexit to barcodes, the post analyses how personal data has become the currency of influence and control.


Originally published in Substack: https://mikkoijas.substack.com/

The climate emergency currently faced by humanity is only one of the pressing concerns regarding the future of civilisation. A large-scale ecological crisis is an even greater problem—one that is also deeply intertwined with social injustice. A third major concern is the rapidly developing situation created by technology, which is also connected to problems related to nature and the environment.

Cracks in the System: Ecology, Injustice, and the Digital Realm

The COVID-19 pandemic  revealed new dimensions of human interaction. We are dependent on technology-enabled applications to stay connected to the world through computers and smart devices. At the same time, large tech giants are generating immense profits while all of humanity struggles with unprecedented challenges.

Brexit finally came into effect at the start of 2021. On Epiphany of that same year, angry supporters of Donald Trump stormed the United States Capitol. Both Brexit and Trump are children of the AI era. Using algorithms developed by Cambridge Analytica, the Brexit campaign and Trump’s 2016 presidential campaign were able to identify voters who were unsure of their decisions. These individuals were then targeted via social media with marketing and curated news content to influence their opinions. While the data for this manipulation was gathered online, part of the campaigning also happened offline, as campaign offices knew where undecided voters lived and how to sway them.

I have no idea how much I am being manipulated when browsing content online or spending time on social media. As I move from one website to another, cookies are collected, offering me personalised content and tailored ads. Algorithms working behind websites monitor every click and search term, and AI-based systems form their own opinion of who I am.

Surveillance and the New Marketplace

A statistical analysis algorithm in a 2013 study analysed the likes of 58,000 Facebook users. The algorithm guessed users’ sexual orientation with 88% accuracy, skin colour with 95% accuracy, and political orientation with 85% accuracy. It also guessed with 75% accuracy whether a user was a smoker (Kosinski et al., 2013).

Companies like Google and Meta Platforms—which includes Facebook, Instagram, Messenger, Threads, and WhatsApp—compete for users’ attention and time. Their clients are not individuals like me, but advertisers. These companies operate under an advertising-based revenue model. Individuals like me are the users whose attention and time are being competed for.

Facebook and other similar companies that collect data about users’ behaviour will presumably have a competitive edge in future AI markets. Data is the oil of the future. Steve Lohr, long-time technology journalist at the New York Times, wrote in 2015 that data-driven applications will transform our world and behaviour just as telescopes and microscopes changed our way of observing and measuring the universe. The main difference with data applications is that they will affect every possible field of action. Moreover, they will create entirely new fields that have not previously existed.

In computing, the word ”data” refers to various numbers, letters or images as such, without specific meaning. A data point is an individual unit of information. Generally, any single fact can be considered a data point. In a statistical or analytical context, a data point is derived from a measurement or a study. A data point is often the same as data in singular form.

From Likes to Lives: How Behaviour Becomes Prediction

Decisions and interpretations are created from data points through a variety of processes and methods, enabling individual data points to form applicable information for some purpose. This process is known as data analysis, through which the aim is to derive interesting and comprehensible high-level information and models from collected data, allowing for various useful conclusions to be drawn.

A good example of a data point is a Facebook like. A single like is not much in itself and cannot yet support major interpretations. But if enough people like the same item, even a single like begins to mean something significant. The 2016 United States presidential election brought social media data to the forefront. The British data analytics firm Cambridge Analytica gained access to the profile data of millions of Facebook users.

The data analysts hired by Cambridge Analytica could make highly reliable stereotypical conclusions based on users’ online behaviour. For example, men who liked the cosmetics brand MAC were slightly more likely to be homosexual. One of the best indicators of heterosexuality was liking the hip-hop group Wu-Tang Clan. Followers of Lady Gaga were more likely to be extroverted. Each such data point is too weak to provide a reliable prediction. But when there are tens, hundreds or thousands of data points, reliable predictions about users’ thoughts can be made. Based on 270 likes, social media knows as much about a user as their spouse does.

The collection of data is a problem. Another issue is the indifference of users. A large portion of users claim to be concerned about their privacy, while simultaneously worrying about what others think of them on social platforms that routinely violate their privacy. This contradiction is referred to as the Privacy Paradox. Many people claim to value their privacy, yet are unwilling to pay for alternatives to services like Facebook or Google’s search engine. These platforms operate under an advertising-based revenue model, generating profits by collecting user data to build detailed behavioural profiles. While they do not sell these profiles directly, they monetise them by selling highly targeted access to users through complex ad systems—often to the highest bidder in real-time auctions. This system turns user attention into a commodity, and personal data into a tool of influence.

The Privacy Paradox and the Illusion of Choice

German psychologist Gerd Gigerenzer, who has studied the use of bounded rationality and heuristics in decision-making, writes in his excellent book How to Stay Smart in a Smart World (2022) that targeted ads usually do not even reach consumers, as most people find ads annoying. For example, eBay no longer pays Google for targeted keyword advertising because they found that 99.5% of their customers came to their site outside paid links.

Gigerenzer calculates that Facebook could charge users for its service. Facebook’s ad revenue in 2022 was about €103.04 billion. The platform had approximately 2.95 billion users. So, if each user paid €2.91 per month for using Facebook, their income would match what they currently earn from ads. In fact, they would make significantly more profit because they would no longer need to hire staff to sell ad space, collect user data, or develop new analysis tools for ad targeting.

According to Gigerenzer’s study, 75% of people would prefer that Meta Platforms’ services remain free, despite privacy violations, targeted ads, and related risks. Of those surveyed, 18% would be willing to pay a maximum of €5 per month, 5% would be willing to pay €6–10, and only 2% would be willing to pay more than €10 per month.

But perhaps the question is not about money in the sense that Facebook would forgo ad targeting in exchange for a subscription fee. Perhaps data is being collected for another reason. Perhaps the primary purpose isn’t targeted advertising. Maybe it is just one step toward something more troubling.

From Barcodes to Control Codes: The Birth of Modern Data

But how did we end up here? Today, data is collected everywhere. A good everyday example of our digital world is the barcode. In 1948, Bernard Silver, a technology student in Philadelphia, overheard a local grocery store manager asking his professors whether they could develop a system that would allow purchases to be scanned automatically at checkout. Silver and his friend Norman Joseph Woodland began developing a visual code based on Morse code that could be read with a light-based scanner. Their research only became standardised as the current barcode system in the early 1970s. Barcodes have enabled a new form of logistics and more efficient distribution of products. Products have become data, whose location, packaging date, expiry date, and many other attributes can be tracked and managed by computers in large volumes.

Conclusion

We are living in a certain place in time, as Dōgen described—an existence with a past and a future. Today, that future is increasingly built on data: on clicks, likes, and digital traces left behind.

As ecological, technological, and political threats converge, it is critical that we understand the tools and structures shaping our lives. Data is no longer neutral or static—it has become currency, a lens, and a lever of power.

References

Gigerenzer, G. (2022). How to stay smart in a smart world: Why human intelligence still beats algorithms. Penguin.

Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behaviour. Proceedings of the National Academy of Sciences, 110(15), 5802–5805. https://doi.org/10.1073/pnas.1218772110

Lohr, S. (2015). Data-ism: The revolution transforming decision making, consumer behavior, and almost everything else. HarperBusiness.

Dōgen / Sōtō Zen Text Project. (2023). Treasury of the True Dharma Eye: Dōgen’s Shōbōgenzō (Vols. I–VII, Annotated trans.). Sōtōshū Shūmuchō, Administrative Headquarters of Sōtō Zen Buddhism.

Encounters Without Preconceptions

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Written by Kikka Rytkönen

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I thought the whole retreat was very good and interesting. Some of the topics that looked boring on paper turned out to be surprisingly engaging. For example, when we visited a Pentecostal church, I initially thought it would be unpleasant for me. But it turned out to be more like a performance, through which I came to understand why people are involved in the movement. It was a good experience.

I also learned how important it is to enter situations without labeling, prejudging, or defining them in advance. Just go in and listen. Everything related to religion was interesting. Lutheran Christianity was the most familiar to me, and perhaps that’s why it didn’t spark quite the same interest. I left the church when I was 16.

I had been to a mosque before, but this time I gained new insight—for instance, I understood why the Tatars haven’t faced discrimination in Finland. They said: “We’ve already been a minority in Russia.” That stayed with me.

Other organizations and begging
The D-station felt cozy. Waiting out the rain together always creates a sense of connection.
VEPA was an amazing place, and the people too. I will definitely return to that place.

Begging was hard. Asking for money felt impossible. People don’t really carry cash anymore. The experience made me feel a bit… submissive, or maybe inadequate. (Please find a better word than “submissive.”)

I ended up chatting with a man around my age standing outside a Euro store. I went inside, and when I came out, he—who turned out to be the shopkeeper—came up to me with a heavy bag full of sausages and chocolate. I thanked him with a handshake. It was a touching moment.

Since then, I’ve given a few euros to people in Piritori who ask for money specifically to buy food. I wonder: is it helpful to give money to someone I suspect is a drug addict? Is it really my place to decide?

Afterthoughts
I also want to mention what happened at the sleeping place. After Maika’s singing and mantra session, others started singing too—it created a beautiful sense of togetherness.

The ceremonies were extremely important and touching for me. On the island, the number of people amplified the experience, and the part of Mikko’s dharma transmission that involved the fire was particularly powerful. Both the content and the ritual form felt somehow purifying. Hard to explain—but I felt very connected.

In the group sharings, it felt like we were family.

Sleeping together so close to others—especially on cardboard and without a pillow—was quite challenging. I tried to learn to enjoy the sounds around me, from birds to some loud noise that made me think, “okay, now the war has started.”When I woke up, I felt congested and hadn’t gotten enough sleep. A sort of regression took over—people started to seem distant, even dismissive of me. I told myself: “Just get through this.” I guess some separation anxiety was already kicking in, knowing it would all end soon.

Back at Elokolo, I fixated on the idea that I needed to eat certain colors at specific intervals, and porridge became my central focus. I probably babbled some nonsense to people there too.

All in all, walking for a day and a half and spending a night without any belongings or a phone was incredibly liberating. It felt good not to have to fuss over stuff, money, or especially a phone.

At the farewell and the restaurant, I clung to Mikko Sensei and Maija—people I knew and felt safe with. I no longer knew how to be with anyone else, even though I could see people having conversations at other tables.

A big THANK YOU for the experience!
Did we become a sangha?

Peace-love,

Kikka

Kikka with Sensei Mikko
Photo by Laura Malmivaara

Kohtaamisia ilman ennakkoluuloja

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Kirjoittanut: Kikka Rytkönen

Koko retriitti oli mielestäni erittäin hyvä ja mielenkiintoinen. Asiat, jotka paperilla näyttivät tylsiltä, osoittautuivatkin paikan päällä todella kiinnostaviksi. Esimerkiksi kun mentiin helluntailaisten kirkkoon, ajattelin aluksi, että tämä varmaan tulee olemaan minulle vastenmielistä. Mutta se olikin oikeastaan kuin esitys, jonka kautta ymmärsin, miksi ihmiset ovat mukana tässä liikkeessä. Se oli hyvä kokemus.

Opin myös sen, kuinka tärkeää on mennä tilanteisiin leimaamatta, ajattelematta tai määrittelemättä mitään etukäteen. Pitää vain mennä mukaan ja kuunnella. Kaikki uskontoon liittyvät osuudet olivat mielenkiintoisia. Luterilainen kristillisyys on minulle tutuinta – ehkä siksi se ei herättänyt ihan samanlaista kiinnostusta. Olen eronnut kirkosta jo 16-vuotiaana.

Moskeijassa olin käynyt aiemminkin, mutta nyt sain siellä uudenlaisen oivalluksen: ymmärsin esimerkiksi, miksi tataarit eivät ole Suomessa joutuneet syrjityiksi. He sanoivat: ”Olemme olleet vähemmistö jo Venäjällä.” Tämä jäi mieleen.

Muut järjestöt ja kerjuu
D-asemalla oli kotoisa tunnelma. Sateen piteleminen yhdessä yhdistää aina. Vepa oli aivan mahtava paikka, ja ihmiset siellä. Vähän harmitti, että Suski sai niin vähän puheenvuoroa. Tähän paikkaan haluan kyllä palata.

Kerjääminen – se oli vaikeaa. Rahan pyytäminen tuntui mahdottomalta. Ihmisillä ei ole enää käteistä mukana. Siinä tilanteessa tuli vähän alistunut, jopa huono olo. (Keksikää parempi sana kuin ”alamainen”.)

Pysähdyin juttelemaan Eurokaupan edessä seisovan, suurin piirtein itseni ikäisen miehen kanssa. Menin kauppaan ja kun palasin, hän – joka olikin kauppias – tuli ulos monen kilon makkara- ja suklaapussin kanssa. Kiitin häntä kädestä pitäen. Se oli koskettava hetki.

Kerjäämisen jälkeen olen antanut Piritorilla ruokaa varten pyytäville ihmisille muutamia euroja. Mietin usein: onko hyödyllistä antaa rahaa, jos epäilen, että henkilö on esimerkiksi päihderiippuvainen? Onko se minun tehtäväni päättää?

Jälkitunnelmia
Täytyy mainita myös nukkumapaikan tapahtumista. Maikan laulatus ja mantrattelu loi upean tunnelman – ja sitten, kun muutkin alkoivat laulaa, syntyi vahva yhteisöllisyyden tunne.

Seremoniat olivat minulle äärimmäisen tärkeitä ja koskettavia. Saaressa ihmismäärä kaiutti tunnelmaa, ja se tulen ympärillä tapahtunut osuus Mikon dharmasiirrossa oli todella voimakas. Sekä sisällössä että muodossa oli jotain puhdistavaa. Syntyi vahva yhdistyneisyyden kokemus.

Ryhmäkeskusteluissa tuntui, kuin olisimme perhettä.

Nukkuminen yhdessä, tosi lähekkäin – varsinkin pahvilla ja ilman tyynyä – oli melko hankalaa. Yritin opetella nauttimaan ympäristön äänistä, välillä linnut, välillä joku valtava ääni josta ajattelin: ”no nyt se sota syttyi”. Herätessä olo oli tukkoinen, ja unta oli selvästi jäänyt liian vähän. Tuli jonkinlainen regressio – ihmiset alkoivat tuntua etäisiltä, melkein kuin ne dissaisivat minua. Ajattelin: ”tästä täytyy nyt vain selvitä”. Ehkä siinä oli jo eroamisen haikeuttakin – kohta tämä kaikki päättyy.

Elokolossa palasin siihen, että mun täytyisi syödä tiettyjä värejä tietyin välein, ja puurosta tuli yhtäkkiä elämän keskiö. Saatoin jutella jotain höpöhöpöäkin ihmisten kanssa.

Kaiken kaikkiaan se, että kuljin 1,5 päivää ja vietin yön ilman omaisuutta, ilman kännykkää, oli todella vapauttavaa. Oli hyvä olla, kun ei tarvinnut sählätä tavaroiden, rahojen tai puhelimen kanssa.

Lopputervehdyksissä ja ravintolassa selviydyin liimautumalla Sensei Mikon ja Maijan viereen – he olivat tuttuja, turvaa tuovia. En enää osannut olla kenenkään muun kanssa, vaikka näin, että joissain pöydissä käytiin keskusteluja.

Suuri KIITOS kokemuksesta!
Tuliko meistä shanga?

Peace-love!

Kikka

Kikka Rytkönen ja Sensei Mikko
Kuva: Laura Malmivaara