Die Moderne:

Alles muss schnell gehen und immer schneller gehen: das ist die Moderne!

Schon so eine Beschleunigung als Schlüsselmerkmal.

Da darf nicht mal, zum Beispiel, ein Monat, ein Sommer, geradezu verträumt werden.

Schönheit innerhalb der eigenen vier Wände:

Das Streben und die Kunst, den eigenen Wohnbereich schön zu gestalten, auch wenn man nicht über den geradezu vollkommenen "Nestbautrieb" einer Frau verfügt.

Warum haben manche Menschen so etwas schrecklich Unerfülltes?

Gutes Handeln:

Die Erfüllung liegt letztlich in einem Handeln, das gewisse Qualitätsansprüche erfüllt.

Moral:

Tagtäglich bestrebt sein, so gut zu handeln, wie es einem irgend möglich ist.

Im Rahmen der eigenen Möglichkeiten maximale Anstrengungen in Richtung gutes Handeln unternehmen.

Average is Over by Tyler Cowen:

ChatGPT:

"Average Is Over, published in 2013, is one of the more provocative books about the future of work, economic inequality, and technological change.

Its central argument is that the ability to work effectively with intelligent machines will become one of the most important determinants of economic success.

Cowen predicts that technological progress will not benefit everyone equally. Instead, it will increasingly divide society into two groups: people who can use intelligent machines to become extraordinarily productive, and people whose skills become less valuable as machines take over more cognitive tasks.

What makes the book particularly interesting is that Cowen does not simply predict that computers will replace human workers. His argument is considerably more subtle.

The future belongs to people who can combine human judgment, creativity, and expertise with the extraordinary computational capabilities of machines.

Let's examine his argument in detail.

1. The end of the average worker

For much of the twentieth century, developed economies provided relatively comfortable lives for millions of workers with ordinary skills.

A person could obtain a reasonable education, find stable employment, perform moderately demanding tasks, and earn a middle-class income.

Cowen argues that this economic arrangement is becoming increasingly difficult to sustain.

Why?

Because computers can perform a growing number of tasks that previously required educated human beings.

Meanwhile, the value of people who know how to direct, interpret, and improve computational systems may increase dramatically.

Cowen's prediction is therefore not simply that machines will replace people.

It is that machines will change which human abilities are economically valuable.

2. Human–machine collaboration: the central argument

This is probably the most interesting part of the book.

The most productive employee may not be the person with the greatest individual intellectual ability, but the person who can most effectively organize and exploit machine intelligence.

3. Why intelligence alone will no longer be enough

Being intelligent and being effective at using intelligence are not necessarily the same thing.

Cowen's argument suggests that the second ability will become increasingly important.

4. The polarization of the labor market

Cowen predicts that intelligent machines will contribute to a growing division between economic winners and losers.

At the top will be people whose abilities complement technology. They may become extraordinarily productive and receive substantial economic rewards.

In the middle, many traditional occupations will come under pressure. Routine analytical, administrative, and technical tasks will increasingly be automated.

At the lower end, some service occupations will remain difficult to automate, but their wages may remain relatively modest.

Cowen imagines a society in which perhaps 10–15% of the population enjoys exceptional prosperity, while many others experience stagnant incomes.

However, his argument contains an interesting paradox.

Even people with relatively low incomes may enjoy access to extraordinary technological capabilities, inexpensive entertainment, information, and education.

In other words, economic inequality could increase even while technology makes certain aspects of everyday life substantially better.

5. Education will change fundamentally

Cowen also questions the traditional educational system.

Historically, obtaining knowledge required access to teachers, universities, libraries, and expensive educational resources.

Intelligent machines dramatically reduce some of these barriers.

A motivated individual can increasingly learn mathematics, economics, programming, statistics, or engineering with computational assistance.

Consequently, the ability to motivate oneself and organize one's learning becomes particularly valuable.

Previously, a person might have needed to attend specialized courses, read numerous technical papers independently, and find experienced colleagues willing to explain complicated mathematical concepts.

6. A particularly important implication: the speed of learning

There is another consequence of Cowen's thesis that deserves attention.

Intelligent machines do not merely increase the productivity of people who already possess considerable expertise.

They can also accelerate the acquisition of expertise itself.

Imagine two equally motivated analysts.

One learns primarily through textbooks, professional experience, and occasional discussions with colleagues.

The other uses the same resources but additionally spends an hour every day discussing difficult economic and mathematical questions with an AI system.

The second analyst might investigate substantially more questions, encounter more modeling approaches, and receive much more immediate feedback.

Over several years, these differences could accumulate.

However, the advantage depends on how the technology is used.

Someone who merely accepts machine-generated answers may develop superficial knowledge.

Someone who repeatedly questions the machine, verifies its conclusions, and implements the underlying ideas may develop much deeper expertise.

The crucial distinction is between outsourcing your thinking and using machines to improve your thinking.

7. What Cowen may have underestimated

We should also examine the limitations of his predictions.

The book appeared in 2013, long before the widespread adoption of modern generative AI.

What happens when machines become so capable that human intervention actually reduces performance?

In such situations, the most valuable human skill might no longer be helping the machine solve a problem.

8. The central lesson

I would summarize Average Is Over in one paragraph:

Tyler Cowen argues that intelligent machines are fundamentally transforming the economic value of human abilities. In the emerging economy, success will increasingly depend on whether individuals can complement computational intelligence rather than compete against it. People who combine expertise, discipline, adaptability, and technological competence may become extraordinarily productive, while those whose abilities can be replaced by machines risk economic marginalization. The resulting society may become significantly more unequal, even as technological progress makes knowledge, education, and many consumer services more accessible.

And I would add one observation particularly relevant to 2026.

The ability to collaborate with intelligent machines may become important not only because it makes you more productive today, but because it enables you to become more knowledgeable, more capable, and more productive tomorrow.

That creates the possibility of a cumulative advantage: better use of AI accelerates learning, which improves judgment, which in turn enables even better use of AI.

And it raises a question worth examining in considerable depth: Could someone who systematically uses AI to deepen their professional knowledge over the next five years acquire expertise that would previously have required fifteen years to develop?

The possibility of substantially accelerating intellectual development is real—and it is one of the most consequential implications of the ideas in Average Is Over."

Why g Matters:

ChatGPT:

"Linda Gottfredson’s article “Why g Matters: The Complexity of Everyday Life”, published in 1997 in the journal Intelligence, is one of the more influential papers arguing that general intelligence plays a major role in people's ability to function successfully in modern society.

Her central argument is fascinating because she moves beyond the traditional question of whether intelligence predicts academic success.

Instead, she asks a much broader question:

Why does general intelligence matter so much in everyday life, and why do its effects become particularly important in a technologically advanced society?

1. What exactly is g?

In intelligence research, the letter g stands for general cognitive ability, sometimes called the general intelligence factor.

The concept originates with the psychologist Charles Spearman.

Researchers observed that people who perform well on one type of cognitive test also tend, on average, to perform well on other cognitive tests.

For example, someone who is particularly good at mathematical reasoning is statistically more likely to perform well on verbal reasoning, pattern recognition, and abstract problem-solving.

These correlations suggest that different intellectual abilities share an underlying component.

Psychologists call this common component g.

Importantly, g does not mean that everyone has the same strengths or weaknesses. A person can be particularly talented in mathematics while having more ordinary verbal abilities.

Rather, g describes a general factor that helps explain why cognitive performance across different domains tends to be positively correlated.

2. Gottfredson's central thesis: Intelligence helps us deal with complexity

This is arguably the most important idea in the entire article.

Gottfredson argues that intelligence becomes especially important when people encounter situations that are complex, unfamiliar, ambiguous, or difficult to predict.

Consider two different activities.

The first involves performing a familiar task according to a fixed routine. The instructions are clear, the environment is predictable, and little independent judgment is necessary.

The second involves solving a problem for which there are no straightforward instructions. Several variables interact, the available information is incomplete, and the consequences of different decisions must be anticipated.

Gottfredson argues that general intelligence is much more important for the second activity.

Why?

Because complex environments require people to identify relevant information, recognize relationships, learn unfamiliar rules, anticipate consequences, and adapt their behavior.

Her central insight is that intelligence is particularly valuable when life cannot be managed simply by following established routines.

This is not merely about solving difficult mathematical problems.

It also concerns understanding financial contracts, following medical instructions, evaluating risks, and making decisions involving several competing considerations.

3. Why intelligence matters in professional life

Gottfredson draws extensively on research into occupational performance.

Her argument is that the predictive importance of intelligence increases with the cognitive complexity of a job.

For example, consider someone performing a highly repetitive task. Once the person has learned the necessary procedure, relatively little additional reasoning may be required.

Now consider an engineer, financial analyst, or software developer.

Such professionals frequently need to understand unfamiliar problems, identify patterns in complicated information, and make decisions without knowing all the relevant facts.

According to Gottfredson, these activities place particularly high demands on general cognitive ability.

She also emphasizes that intelligence facilitates learning. People with higher measured cognitive ability tend, on average, to acquire complicated job knowledge more readily.

This creates an important connection between intelligence, learning, and professional performance.

A person who learns complex procedures more quickly can accumulate useful knowledge and apply it to increasingly demanding tasks.

However, experience, conscientiousness, motivation, and specialized knowledge also matter. Intelligence is an important predictor, not a complete explanation of professional success.

4. Intelligence and the demands of everyday life

One of the article's most interesting contributions is Gottfredson's attempt to demonstrate that intelligence matters far beyond the workplace.

She uses evidence from the American National Adult Literacy Survey.

The survey examined people's ability to handle practical information encountered in ordinary life.

Examples include understanding documents, interpreting tables, working with financial information, and using transportation schedules.

Gottfredson argues that these activities involve the same general cognitive processes required in more obviously intellectual settings.

The difference between understanding a complicated financial contract and solving an abstract reasoning problem is therefore not as fundamental as it might initially appear.

Both require identifying relationships, processing information, and drawing appropriate conclusions.

Her broader point is that modern societies continuously confront individuals with cognitive demands.

Intelligence influences how easily people can navigate these demands, even when the activities do not appear particularly intellectual. 

5. Small advantages accumulate over a lifetime

Another important argument concerns cumulative effects.

Imagine two people who differ moderately in their ability to learn unfamiliar information.

At any particular moment, the difference might appear relatively small.

However, across thousands of situations involving education, employment, financial decisions, and everyday problem-solving, small differences in the probability of successful outcomes can accumulate.

Gottfredson argues that this helps explain why general intelligence is statistically associated with important differences in life outcomes.

Her argument is probabilistic: cognitive ability changes the likelihood of certain outcomes without making those outcomes inevitable.

6. The problem of increasing complexity

This brings us to the article's broader societal implications.

Gottfredson suggests that modern economies increasingly reward the ability to handle complicated information.

Technological development can eliminate some routine activities while increasing the importance of tasks requiring judgment, abstraction, and independent learning.

Consequently, individuals who find complex information processing difficult may encounter growing disadvantages.

She therefore considers how society might make important information and everyday systems easier to understand.

This is an essential distinction: her article does not merely argue that intelligence matters. It also raises the question of how institutions can accommodate differences in cognitive ability.

7. How does this connect to artificial intelligence?

Gottfredson published her article in 1997, long before contemporary generative artificial intelligence.

Nevertheless, her analysis provides an interesting framework for understanding AI.

This creates another important question: Does artificial intelligence reduce the importance of general intelligence, or does it increase the rewards associated with certain cognitive abilities?

There are arguments in both directions.

AI can make complex activities accessible to people who previously lacked the necessary technical knowledge.

At the same time, people who are particularly good at identifying problems, evaluating answers, recognizing errors, and integrating information may be able to extract greater benefits from these systems.

Tyler Cowen argues that the ability to work effectively with intelligent machines can become an increasingly important source of economic advantage.

Gottfredson provides a psychological explanation for why such differences might emerge: working effectively with sophisticated technologies can itself require substantial cognitive ability.

However, this is an extension of her framework, not a finding established by her 1997 paper.

8. How should we evaluate Gottfredson's argument critically?

I think three distinctions are particularly important.

First, intelligence is not identical to knowledge. A highly knowledgeable person may possess extensive expertise in a particular field, while another person may be exceptionally good at learning unfamiliar concepts.

Second, intelligence is not identical to good judgment. Emotional regulation, experience, personality, and the ability to recognize one's own mistakes also influence decision-making.

Third, correlations between intelligence and life outcomes do not establish that intelligence alone causes those outcomes. Educational opportunities, economic resources, health, and social circumstances can also matter.

Gottfredson's strongest argument concerns the relationship between cognitive ability and task complexity. Her broader interpretations of social outcomes require more caution.

9. The central conclusion

I would summarize the paper in one sentence:

General intelligence matters because modern life repeatedly requires people to understand complexity, learn unfamiliar information, solve problems, and make decisions under uncertainty.

The greater these cognitive demands become, the more consequential differences in cognitive ability can be."