The Next Stop of Normal Regression
in the Electronic Age:
The Violent Revolution of Technology

This paper proposes a unified analytical framework spanning the technological, cognitive, and civilizational layers. The core thesis is: recommendation algorithms, big data, and artificial intelligence share a single mathematical feature — sorting by probability — whose long-term operation inevitably compresses distribution variance, a trend this paper terms “normal regression.” It should be noted that “normal regression” is not used here in the strict statistical sense of “regression to the mean” (where extreme values statistically regress toward the mean in subsequent measurements), but rather in its intuitive sense — a dynamic process of systematic convergence toward the center of the distribution, continuous disappearance of the tails, and irreversible compression of variance. This process not only causes AI model collapse but may also propagate through the homogenization of the information environment to the human cerebral cortex, leading to the degradation of cognitive mapping capabilities and generational rupture in physical-world interaction skills among the post-2000 generation. Meanwhile, the systematic suppression of industrial capital by financial capital continuously marginalizes “value creators” within the distribution system. History shows that when the mismatch between technological capability and distributional weight accumulates to a critical point, the system self-corrects through violent means. This paper asks: when all of humanity simultaneously experiences cognitive collapse and physical skill rupture, can this correction mechanism still function?

Three Sorting Mechanisms, One Destiny

The essence of recommendation algorithms is similarity sorting — continuously reducing the distance between data points in high-dimensional space, clustering users toward the center of behavioral clusters. The essence of big data is probability sorting — assigning higher weights to high-frequency patterns, rendering low-frequency events statistically invisible. The essence of large language models is token probability sorting — generating outputs along maximum-likelihood paths in language space, with each step favoring the highest-probability next token.

It should be noted that LLMs employ sampling strategies such as temperature, top-k, and top-p during actual generation to inject artificial randomness and avoid fully deterministic outputs. These mitigation mechanisms do briefly restore output diversity — but they are fundamentally the same type of operation as the “alignment metric rotation” discussed in Section 02: local perturbations applied to an already compressed distribution, which do not alter the underlying trend of variance compression. When models are recursively trained on their own outputs, regardless of how sophisticated the sampling strategy, the tails of the distribution still vanish irreversibly.

All three share the same mathematical feature: each iteration compresses the variance of the output distribution. When variance shrinks below a critical threshold, the system loses its ability to distinguish between different inputs. In AI, this is called “model collapse”; in information theory, it corresponds to the heat death state within the entropy-increase process; in statistics, it is the continuous convergence of the distribution toward its peak.

The landmark 2024 study by Shumailov et al. published in Nature confirmed that recursive training on AI-generated data leads to irreversible degradation — the tails of the original content distribution vanish first, and the model is “poisoned” by its own projection of reality. A follow-up study at ICLR 2025 further demonstrated that this feedback loop causes models to continuously reinforce errors and biases in synthetic data.

Alignment Metric Rotation: The Technical Means of Concealing Collapse

When normal regression on one alignment metric causes discriminatory power to vanish, the industry’s standard practice is to switch to a new metric and continue extracting residual variance. From click-through rates to dwell time, from completion rates to interaction rates, from “user satisfaction” to “helpfulness” scores — each metric switch briefly restores the system’s discriminatory capacity, but the underlying entropic trend remains unchanged.

This has a precise correspondence in thermodynamics: you can create temporary temperature differentials through localized work in a closed system, but the system’s total entropy continues to increase. Metric rotation is precisely this kind of localized work — it delays the manifestation of collapse while simultaneously accumulating greater corrective potential energy.

Research presented at the 2024 WWW Conference decomposed the effects of recommendation systems into two independent dimensions: inter-user diversity and intra-user diversity. The study found that traditional recommendation algorithms primarily reduce filter bubbles by decreasing inter-user differences — essentially replacing filtration with homogenization. Facebook’s news feed filtering reduced users’ exposure to opposing viewpoints by 15%.

Normal Regression of the Cerebral Cortex

The core function of the cerebral cortex is to establish differentiated mappings — mapping different sensory inputs to distinct internal representations. The richness of these mappings depends on two conditions: input diversity and processing depth. When a person’s information input is pre-filtered by algorithms into a highly homogenized content stream, the signal diversity received by the cerebral cortex drops precipitously.

Neuroplasticity means the brain adapts to this low-variance input. Synaptic connections optimize for processing homogeneous information, while pathways for processing heterogeneous information gradually weaken due to lack of activation. This is not a metaphor but a real neurobiological process. European researchers have named this phenomenon “clip thinking” — a cognitive mode of rapid scanning, skimming, and slice-by-slice information processing.

Jonathan Haidt, in The Anxious Generation, identified that a “Great Rewiring” of humanity occurred between 2010 and 2015 — smartphones became “experience blockers,” consuming vast amounts of time that would otherwise have been spent on physical play and face-to-face interaction. His data shows that fifty years of continuous progress in educational achievement metrics came to an abrupt halt in 2012.

Deeper research has found that internet use directly alters brain activity patterns — regional homogeneity in the middle temporal gyrus decreases (involving object recognition), and functional connectivity between the temporal gyrus and parahippocampal cortex weakens (involving memory encoding and retrieval). Brain development in early life exhibits high plasticity; plasticity in adulthood is more environmentally controlled. This explains why the post-2000 generation is most deeply affected — their high-plasticity window was entirely immersed in a low-variance information environment.

The Age of Pseudo-Individuality: Conformist Performances of Uniqueness

The Chinese artist and critic Chen Danqing made a precise observation in a conversation: young people born after 2000 are unable to define a problem before they begin speaking. This is not a matter of intelligence, but rather that the information environment in which they grew up never required them to complete this “preliminary step.” The interaction patterns of short videos, bullet comments, and comment sections reward immediate reactions, not structured expression.

American research presents the same picture. Gen Z continuously struggles between “individual expression” and “trend-following” — social media trends change so fast that they are chasing a constantly moving target. Researchers have observed a deep paradox: this generation has learned to celebrate the concept of “individuality” amid extreme conformity, simultaneously being both aware of and imprisoned by hive-mind thinking. Everyone is claiming to be “different” in exactly the same way.

The Chinese intellectual Qian Liqun has characterized this era as one of “no truth, no consensus, no certainty.” These three absences have different roots but mutually reinforce each other: the collapse of truth-verification mechanisms (disintegration of institutional credibility), the fracturing of the factual foundation for consensus (algorithms place everyone in a different information slice), and the hunger for certainty driven by pervasive uncertainty (extreme rhetoric and simplified narratives provide substitutes for certainty, not truth itself).

It is necessary to address a common objection: every generation believes it faces an unprecedented crisis, so how is today’s judgment any different from past intergenerational complaints? The answer lies in the discontinuous magnitude of change. Haidt’s data shows that U.S. adolescent depression and anxiety rates experienced a cliff-like surge between 2010 and 2015 — depression rates increased 134%, anxiety rates increased 106%. Non-fatal self-harm rates among girls aged 10 to 14 more than quintupled during the same period. This is not gradual change but a mutation occurring within the precise time window of mass smartphone and social media adoption. No technological change in history has ever produced such synchronized psychological health impacts on such a broad population in such a short period. This magnitude differential is the critical anchor distinguishing the current situation from past “each generation worse than the last” narratives.

The Great Retreat from the Physical World

Human behavioral patterns are undergoing a systematic migration from the physical world to the electronic one. Survey data shows that Gen Z spends 15 fewer minutes outdoors daily than Gen X; male participation in scouting and outdoor activities has declined from 67% among Baby Boomers to 42% among Gen Z; face-to-face social interaction is becoming increasingly uncommon. Meanwhile, 88% of Gen Z regularly participates in online gaming, with virtual environments becoming the primary venue for peer interaction.

The World Economic Forum’s 2025 Future of Jobs Report marks a historic inflection point: the skill dimension of “manual dexterity, endurance, and precision” showed a net negative decline expectation for the first time — the declining relevance of physical capabilities, which had only been a trend in previous reports, was predicted as a net decrease for the first time. Employers widely report that graduates trained through academic channels are technically qualified but lack teamwork, troubleshooting, practical knowledge, and safety habits — skills required in the physical world.

Here emerges a paradox overlooked by most: precisely because digitalization is eroding physical-world skills, physical-world capabilities are becoming the scarcest resource. 2025 Federal Reserve data shows that the unemployment rate for liberal arts graduates is currently about half that of computer science and engineering graduates. AI may replace a large volume of technical cognitive work, paradoxically increasing demand for physical skills (traditional craftsmanship) and interpersonal non-cognitive skills.

A possible counterargument: young workforces in developing countries such as India and Southeast Asia still retain relatively strong physical skills — could they constitute an “external backfill” mechanism? This argument has partial validity — global labor mobility can indeed alleviate developed countries’ physical skill gaps in the short term. However, two structural factors limit the effectiveness of this backfill. First, developing countries themselves are digitizing at accelerating rates — India’s smartphone penetration has exceeded 70%, and TikTok’s average daily usage time in Southeast Asia is approaching Western levels — the homogenization time lag is shrinking rapidly. Second, high-complexity infrastructure (nuclear facility maintenance, chip fabrication, deep-sea communications cables) requires not merely manual skills but tacit knowledge accumulated over decades within specific technological ecosystems — knowledge that cannot be rapidly transferred through labor mobility.

The Wisdom of the Mongols and the Cyberpunk Illusion

The Mongol Empire — the most powerful engine of violence in human history — had an iron rule when sacking cities: artisans, doctors, and engineers were not killed but sorted and organized into specialized teams. Genghis Khan employed Yelu Chucai not out of admiration for Confucianism, but because without him, the empire’s administrative and taxation systems could not function. As conquerors who themselves lacked the capacity to build, the Mongols understood the value of physical technical capability better than anyone.

This reveals a civilizational principle spanning millennia: violence can conquer the world, but violence cannot sustain it. Every civilization that outsourced its physical technical capabilities eventually paid the price. The Roman Empire outsourced its military to Germanic mercenaries and its engineering maintenance to provincial technicians — when these outsourcing chains broke, aqueducts collapsed with no one to repair them, and Rome’s population plummeted from over a million to a few tens of thousands. It must be noted, of course, that the fragmentation of the Mongol Empire and the decline of Rome were multi-causal — succession systems, geographic dispersion, military pressure, and plague all played intertwined roles. The outsourcing of technical capability was an important necessary condition in these cases but does not constitute a sufficient explanation. This paper invokes these historical analogies to illustrate the systemic risk that arises when physical technical capability is severed, not to reduce complex civilizational decline to a single factor.

The current situation is more dangerous than any in history: past technological outsourcing was spatial (from one group to another that still possessed capability), while today’s is temporal (from the current generation to automation systems that do not yet exist); past ruptures were local (when one civilization collapsed, others still stood), while today’s is globally synchronized; past infrastructure was relatively simple (stonemasons could rebuild aqueducts), while today’s power grids, chip fabrication facilities, nuclear installations, and deep-sea communications cables require decades of accumulated experience.

The “Cyberpunk 2077” vision of the future reinforces this spiral — when young people’s future expectations are anchored in a fully digitized world, physical-world skills sink further in their value hierarchy. It’s not that they don’t know pipes can break; they subconsciously assume “there’ll be robots to fix it by then.” This cognitive expectation itself reduces their motivation to invest in learning physical skills.

Civilization’s Dark Thread: The Eternal Struggle Between Value Creators and Value Distributors

A recurring dark thread runs through the development of human civilization: in the early phase, technicians and builders establish the foundations of development by transforming the physical world; in the later phase, elites, opportunists, and socially dominant groups seize the fruits of development by rewriting the rules of distribution.

Every civilizational cycle follows the same script. Act One: capability equals power — those who can build things hold distributional advantage. Act Two: once infrastructure matures, symbol manipulators (financiers, real estate developers, information intermediaries) gradually seize distributional power — because in a mature system, the power to set distribution rules is worth more than production capability. Act Three: distributional imbalance accumulates to a critical point, and the system self-corrects through crisis.

Academic research has validated this dark thread from multiple angles. Regression analysis of EU panel data found that the financialization process has had significant negative effects on both the value added and employment of the industrial sector — deindustrialization is fundamentally driven by financialization. Turchin, in End Times, identifies wealth inequality as one of two leading harbingers of societal collapse (the other being elite overproduction). Scheidel, tracing the history of inequality since the Bronze Age, finds that large-scale violence is the most “effective” mechanism for wealth redistribution.

The deeper logic of those who launch wars is often linked to the suppression of technological capability — though this is a central thread within an explanatory framework, not the sole cause. The outbreak of World War I involved multiple factors including alliance systems, nationalism, imperial competition, and contingent events; the mismatch between technological capability and distributional power is only one key dimension. This paper’s position is that this dimension has been severely underestimated in existing scholarship and deserves to be placed at the center of analytical frameworks, but without excluding other explanatory factors.

Consider pre-WWI Germany as an example: it possessed the world’s strongest industrial technology, yet the distributional power of the global colonial system was locked in by established empires. The Third Reich achieved astonishing technological leaps in 12 years — the V-2 ballistic missile, the Me 262 jet fighter, and nuclear fission research. But Nazi Germany’s case also reveals the limits of a “technology-first” framework — Hitler expelled Jewish scientists for reasons of racial ideology, directly damaging Germany’s technological capability. These expelled geniuses later became the core of the Manhattan Project. This demonstrates that ideology and technological rationality exist not in a simple “front stage/backstage” relationship but in genuine conflict and mutual impairment. Ideology is the fuel for social mobilization; technological capability is the determinant of war’s trajectory, but the two can constrain each other. Every postwar revival — the Marshall Plan, the German and Japanese economic miracles — was an era in which technical workers were most valued, because once the symbolic systems were destroyed, distributional power temporarily returned to those most capable of creating physical value.

A Correction Cycle Without Correctors

Converging all preceding threads, a complete causal chain emerges:

Historically, the prerequisite for every correction of distributional imbalance was the existence of a group whose technological capabilities were suppressed but still intact — who would return to the top of the value chain after the system reset. But the current situation is unprecedented: all of humanity is simultaneously experiencing cognitive collapse and physical skill rupture. The younger generations in the United States, China, Europe, and Japan are all simultaneously experiencing atrophy in their capacity to manipulate the physical world, with no external civilization available to step in.

This means that even if a systemic crisis occurs, there may not be enough people to complete a “postwar revival.” Between the exit of the older generation’s physical capabilities and the maturation of automated systems, there may be a vacuum period of 20 to 30 years. During this vacuum, the quality of physical-world maintenance will inevitably decline.

The symbolic systems constructed by contemporary financial capital and digital platforms are far more complex and self-healing than at any point in history — they can delay collapse by continuously rotating alignment metrics, just as recommendation algorithms keep switching metrics to mask model collapse. This pushes back the timing of correction, but the intensity of that correction continues to accumulate.

This may be the first distributional imbalance cycle in history without a correction mechanism. The mathematical logic of normal regression will not stop running simply because we become aware of it. The only question is: will the rigid constraints of physical reality — pipes will leak, power grids will age, bridges will collapse — force humanity to re-prioritize the physical world before the cognitive infrastructure is fully “cyberized”?

The answer depends on a race: will the rebound speed of physical reality or the irreversible speed of cognitive collapse reach the critical point first? We can sketch three possible scenario paths:

Total Normal Regression: No Domain Spared

The preceding eight sections have argued across dimensions of algorithms, information, cognition, behavior, economics, and history. But this dimension-by-dimension narrative itself is misleading — it implies that normal regression is a localized problem in a few domains. The reality is: normal regression in the electronic age is a simultaneous process across all of humanity, all domains, and all dimensions. In every human activity captured by probability-sorting logic, variance is being compressed. No domain is spared.

Science. Publication systems rank by citation count, funding ranks by prior results, peer review ranks by paradigm conformity. The result: the share of disruptive papers has plummeted 91–100% since 1945 (Park et al., Nature 2023). NIH principal investigators under age 35 have dropped from 18% to 2%. Hundreds of billions of dollars in annual research funding are producing certainty with industrial efficiency — and certainty is the antonym of paradigmatic leaps. Einstein wrote the theory of relativity in a patent office. Shannon created information theory in a single paper. Their shared characteristic was not more funding but sufficient uninterrupted thinking time and a cognitive position sufficiently far from the mainstream. The contemporary research system systematically eliminates both conditions.

Journalism. Algorithms rank by click-through rates and emotional reactions. In-depth investigative reporting requires readers to invest twenty minutes of sustained attention and tolerate uncertainty — which directly conflicts with algorithmic optimization. The result: flash reports replace analysis, emotion replaces facts, fragments replace continuous narrative. Readers’ thought chains can no longer map multi-layered information.

Education. Standardized tests rank by accuracy rates, school rankings rank by college admission rates, teacher evaluations rank by student satisfaction scores. The result: the education system rewards memorization and compliance, punishing questioning and deviation. Students are trained to give “correct answers,” not to challenge premises.

Politics. Elections rank by vote count, polls rank by approval ratings, policies rank by short-term public opinion feedback. The result: politicians converge toward the median voter, policies converge toward the lowest common denominator, and long-term strategic thinking is crushed by electoral cycles. Politicians who dare to propose counterintuitive solutions are eliminated by probability sorting.

Art. Streaming platforms rank by play count, galleries rank by market price, publishers rank by projected sales. The result: algorithmically predictable content gains distribution, while unpredictable creation is buried. Music sounds increasingly alike, films look increasingly alike, novels read increasingly alike — not because creators lack imagination, but because the distribution system’s probability sorting rewards similarity and punishes heterogeneity.

Business. Investments rank by historical return rates, hiring ranks by resume keyword match, products rank by user ratings. The normal regression of venture capital is stark — the top 30 firms raised $49 billion, while 188 emerging firms combined raised only $9.1 billion. Capital floods toward validated models, avoiding genuine uncertainty. More and more money, less and less variance.

An Inventory of Counter-Forces: Why They May Not Be Enough

Any serious analysis must confront counter-evidence. There are indeed multiple forces currently attempting to reverse the trend of normal regression.

At the policy level, Australia has legislated a ban on social media for those under 16; the global movement of “phone bans in schools” is expanding; Haidt’s proposed four reforms — no social media under 16, no smartphones before high school — have gained policy responses in multiple states and countries. At the educational level, the maker space movement, the return of hands-on STEM education, and the revival of apprenticeships are all happening — the U.S. NSF’s IGE program is redesigning graduate STEM education to incorporate interdisciplinary practice and industry collaboration. At the cultural level, “digital detox” and “screen-free childhood” counter-consumer-digital cultures are gaining traction among young parents.

However, these counter-forces face a structural dilemma: their own channels of propagation are subject to the very system of normal regression. A campaign urging people to “put down their phones and go into nature” must rely on algorithmic recommendation to reach its target audience; a maker space’s enrollment advertisement must compete with TikTok short videos for attention resources. Counter-forces are inherently disadvantaged in propagation efficiency — because what they are trying to do (increase cognitive friction, extend deep thinking time, engage with the inconvenience of the physical world) is precisely what the algorithmic optimization mechanism seeks to eliminate.

China’s case merits special discussion. China is currently the only major power that simultaneously retains a large-scale manufacturing workforce while experiencing high-speed digital disruption. In 2020, over 40% of Chinese university graduates received STEM degrees (compared to 20% in the United States). The Chinese government’s restrictions on gaming time for minors, its crackdown on the private tutoring industry, and its policy tilt toward “specialized, refined, distinctive, and innovative” manufacturing can all be understood as national-level hedges against the trend of normal regression. Yet, the short-video consumption duration, social media dependency, and outdoor time shrinkage among China’s younger generation show no fundamental difference from global trends. China’s manufacturing advantage stems more from the accumulation of the previous generation and current middle-aged workforce than from new capabilities of the post-2000 cohort. This means China may enjoy a longer buffer period than other countries but has not truly escaped the closed loop of normal regression.

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