Sisyphus at the Desk

There was once a man condemned to push a stone up a mountain.

The gods were precise in their cruelty. The stone would roll back down each time. The effort would never conclude. The meaning would never arrive.

Today the mountain has been replaced by an office.
The stone has become a document.
It returns in the form of revisions.

We were told that machines would free us.

In 1930, John Maynard Keynes predicted that technical progress would soon grant humanity a fifteen-hour workweek. He believed that once material needs were satisfied, we would confront the deeper question: what shall we do with our freedom?

The machines came.
The freedom did not.

Agricultural labor collapsed. Industrial labor shrank. Output multiplied. Yet the hours persisted. The factory gave way to the office; the tool to the interface. The stone changed its texture but not its demand.

It would be naïve to claim that all modern labor is useless. The world is intricate. Hospitals require coordination. Bridges require engineering. Goods require logistics. Complex societies produce complex roles.

And yet, beneath the coordination, something unsettles.

There are entire days composed of motion without encounter. Emails answered to maintain flow. Meetings convened to schedule other meetings. Documents prepared for internal circulation, read briefly, then archived. Nothing collapses. Nothing transforms. The system sustains itself.

The absurd does not shout in such places.
It hums.

Albert Camus wrote that the absurd is born of the confrontation between the human need for meaning and the unreasonable silence of the world. In the modern office, the silence is procedural. The longing is quiet. We wish to see the mark of our effort in the world — a repaired engine, a healed body, a shaped piece of wood. Instead we see metrics.

The stone rolls, but invisibly.

We have no tyrannical gods. There is no decree from Olympus. The structure persists without conspiracy. Bureaucracy grows because complexity grows. Risk demands documentation. Regulation demands proof. Institutions demand internal reassurance. No villain is required. Only incentives.

And now a new promise arrives.

Artificial intelligence drafts the memo. It summarizes the report. It answers the inquiry. Once again we are told that liberation approaches.

The stone becomes lighter in one dimension. Heavier in another.

For each task automated, another appears: oversight, compliance, audit, supervision of the machine. We no longer push the stone alone; we monitor its trajectory. The mountain is now algorithmic.

The absurd sharpens not because we work, but because we work without clarity. We are told our labor is necessary. Perhaps it is. Yet necessity becomes abstract. The consequences are statistical, diffused across systems too large to grasp.

If a nurse vanished, suffering would announce itself.
If a mechanic vanished, engines would stall.
If entire departments vanished, the effect might take time to detect.

This is not proof of futility. It is proof of distance.

Work has become moralized. To be busy is to be good. To be idle is to be suspect. Even in abundance, we hesitate before leisure. We fear the silence that would follow.

So we continue.

Sisyphus, in Camus’ telling, is not tragic because he pushes the stone. He is tragic only if he believes in its promised conclusion. The revolt begins in lucidity. He sees the condition clearly and does not appeal to false hope.

What would lucidity look like at the desk?

It would not require the destruction of work. It would require the refusal to sanctify it. It would mean admitting that productivity and purpose are not identical. It would mean asking whether the stone must always be lifted, or whether we have mistaken habit for necessity.

The machines have fulfilled their promise. They have increased our capacity. They have multiplied our output.

They have not answered the question.

If the workweek remains long, it is not because the stone is heavy. It is because we continue to organize our dignity around pushing it.

The absurd remains.

Not in catastrophe.
Not in oppression.
But in fluorescent light, in climate-controlled rooms, in the steady rhythm of revision.

The mountain is now horizontal.

And still, we push.

Yet there is a moment — quiet, almost imperceptible — when the stone rolls back and we descend the slope to retrieve it. In that pause, the task loosens its hold. The structure does not vanish, but its authority weakens. We see the repetition without pretending it is destiny.

That moment is small. It changes nothing outwardly. The email will still be answered. The document will still be revised.

But in that lucidity, something shifts.

The freedom is not in abandoning the stone.
It is in knowing we are the ones who lift it.

And in that knowledge, however modest, the revolt begins.

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Fixing Inequality and Capital Ownership

Most conversations about inequality focus on wages, taxes, and government programs. But that misses the deeper divide shaping today’s economy.

The real gap is not just who earns income — it is who owns the assets that generate it. As more wealth is created by markets, machines, and financial capital rather than human labor, an economy built almost entirely around wages becomes structurally unstable. If we want to confront inequality at its root, we have to stop treating ownership as a luxury — and start treating it as economic infrastructure.

A Universal Capital Ownership program is designed to do exactly that — without abolishing private markets.


What is Universal Capital Ownership?

Universal Capital Ownership is simple in concept:

A dedicated tax is used to purchase broad stock and bond index funds, and the shares are distributed to citizens over time.

Private ownership remains intact.
Markets continue to function normally.
The only structural change is that ownership becomes broadly distributed instead of highly concentrated.


Why this matters

Today, income is still primarily distributed through jobs.
But an increasing share of national income now comes from capital rather than labor.

This creates a structural mismatch:

most people depend on wages, while more of the economy’s gains flow to owners of capital.

Broad participation in retirement accounts does not solve this problem.
What matters is not how many people hold a financial account — it is who owns enough assets to receive meaningful capital income and financial security.


How Universal Capital Ownership would work

The asset

A small set of ultra-low-cost, whole-market index funds covering stocks and bonds.

The funding

A dedicated payroll-style tax (for example, around 8 percent of income above a basic exemption).
The program is defined-contribution, not defined-benefit:

whatever is collected is invested and distributed.

The distribution

Shares are allocated regularly to citizens, primarily based on:

  • hours worked (with an annual cap), and
  • parallel provisions for disability, caregiving, new adults, and newborns.

Guardrails

  • Shares for minors are held in trust.
  • New shares vest after a short delay.
  • Individuals may later move their holdings to private institutions.

A complementary reform: a capital-gains exemption for ordinary owners

To reinforce the goal of broad capital ownership, Universal Capital Ownership should be paired with a simple tax reform:

No capital-gains tax on the first $1 million of lifetime realized gains per person.

This exemption is designed to support ordinary and first-time wealth builders — not large investors.


Why this complements Universal Capital Ownership

Universal Capital Ownership expands access to capital.

A lifetime capital-gains exemption ensures that when people finally do build assets, they are not treated the same as large investors whose primary income already comes from capital.

Together, the two reforms address both sides of the ownership problem:

  • Universal Capital Ownership creates ownership.
  • The exemption protects and rewards early and modest ownership.

Using a lifetime threshold (rather than an annual one):

  • targets long-term wealth building,
  • discourages short-term trading strategies, and
  • prevents repeated use of the exemption by high-frequency or professional investors.

Large investors would still pay capital-gains taxes on gains above the exemption.


What this combined approach would change

  • Capital income would be distributed to a far broader share of the population.
  • Households would gain asset buffers, not just paychecks.
  • Over time, economic and political power would become less concentrated.

What it would not automatically fix

  • Housing costs, healthcare costs, or regional price pressures.
  • Market volatility.
  • Inequality rooted in private business ownership and real estate (unless expanded later).

Universal Capital Ownership versus Universal Basic Income

A UBI distributes cash income.

Universal Capital Ownership distributes ownership.

UBI can reduce poverty in the short term.
Universal Capital Ownership changes who owns the productive economy in the long term.

They are not substitutes — they solve different problems.


The real design challenges

Any serious version of Universal Capital Ownership must address:

  • how voting rights and fund governance are handled,
  • how early selling and financial emergencies are treated,
  • market-timing and cohort risk, and
  • how the program integrates with existing safety nets.

Bottom line

Reducing inequality requires changing who benefits when the economy grows.

A Universal Capital Ownership program — paired with a lifetime capital-gains exemption for ordinary investors — creates a second pillar of economic citizenship:

wages plus ownership, rather than wages alone.

In an economy where capital income is increasingly important and ownership remains highly concentrated, this approach targets inequality at its source — by changing who owns capital itself.

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Where is Everybody?

On a truly dark night, a star-filled sky feels impossibly vast.
But what we are actually seeing is only a tiny, local patch of our own galaxy.

That feeling of scale leads to the same question that physicist Enrico Fermi famously asked:

“Where is everybody?”

This question is now known as the Fermi Paradox.


The basic puzzle

We know three things:

  • The universe contains an enormous number of stars and galaxies.
  • Many stars have planets.
  • Some of those planets probably have environments where life could exist.

So it seems natural to ask:

Why don’t we see any clear evidence of intelligent civilizations?

Not in radio signals.
Not in large-scale engineering.
Not in obvious technological footprints.

Organizations like SETI have searched for decades, and so far, no confirmed artificial signal has been detected.


A crucial correction

It is tempting to turn this into a numbers game:

“If even a small percentage of planets develop life and intelligence, then there should be huge numbers of civilizations.”

But in reality:

we do not know the probabilities for the most important steps.

In particular, we have almost no data for:

  • how often life actually begins,
  • how often complex life evolves,
  • how often intelligence appears,
  • how long technological civilizations survive.

So large numerical estimates (for example, “100,000 civilizations in our galaxy”) are thought experiments, not scientific measurements.

They illustrate uncertainty — they do not constrain reality.


How advanced civilizations are often described

A common way of classifying hypothetical civilizations is the Kardashev scale, which groups societies by how much energy they can use:

  • Type I – uses most of the energy available on its planet
  • Type II – uses energy on the scale of its star
  • Type III – uses energy on the scale of its entire galaxy

The scale is useful for imagination, but it is important to remember:

no Type II or Type III civilization has ever been observed.

Ideas such as star-encircling megastructures are theoretical, not expected or required outcomes of technological progress.


Why the lack of evidence is puzzling — but not decisive

It is often argued that a sufficiently advanced civilization could spread across the galaxy in a relatively short cosmic time.

That may be physically possible.

However, this depends on assumptions that are not scientific facts:

  • that civilizations want to expand,
  • that expansion remains safe and sustainable,
  • that large-scale engineering is desirable,
  • and that societies behave in broadly human ways.

Because of this, the absence of galaxy-wide activity does not logically imply that advanced civilizations do not exist.


Two broad classes of explanations

1. Advanced civilizations are extremely rare or never arise

A popular way of framing this idea is the Great Filter.

The Great Filter proposes that somewhere between:

simple chemistry → life → intelligence → advanced technology

there is a step that is extraordinarily unlikely.

This framing is strongly associated with philosopher Nick Bostrom.

Depending on where that filter lies, three possibilities follow:

  • The filter is behind us
    → intelligent life is extremely rare.
  • The filter is ahead of us
    → many civilizations reach our level and then fail.
  • The universe is only now becoming friendly to life
    → we may be among the first technological species.

A key correction:

Although some evolutionary transitions on Earth took a very long time, this alone does not prove they are universally rare.
We only have one known biosphere, so we cannot reliably infer how common most biological steps are.


2. Advanced civilizations exist, but we do not detect them

Another family of explanations assumes intelligent life is common, but invisible to us for practical reasons, such as:

  • we are searching a tiny fraction of possible signals,
  • we may be looking in the wrong ways or at the wrong times,
  • advanced societies may avoid broadcasting,
  • or their activity may not resemble what we imagine.

Scientists such as Carl Sagan have long emphasized how narrow our current search window really is.

More speculative ideas—sometimes discussed by researchers and science communicators such as
Stephen Hawking and Michio Kaku—include:

  • deliberate non-contact (“zoo” scenarios),
  • civilizations that no longer inhabit physical space in recognizable ways,
  • or forms of intelligence that we would struggle to recognize at all.

These ideas are logically possible, but they are not testable today.


The real scientific situation

The heart of the Fermi Paradox is not astronomy.

It is biology and survival.

We currently do not know:

  • how fragile life is,
  • how often intelligence emerges,
  • or whether long-lived technological civilizations are common or extremely rare.

Our lack of detections is consistent with many very different realities.


A more accurate conclusion

The silence of the sky does not yet tell us whether:

  • we are rare,
  • we are early,
  • or we are simply difficult to notice.

What it does tell us is something more uncomfortable and more honest:

we still understand very little about the path from life to lasting technological civilization.

The Fermi Paradox is therefore less about aliens—and more about the limits of our knowledge, and the long-term future of our own species.

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The Lens of Zero-Sum Thinking

Imagine believing that for someone else to succeed, you must fail. This way of thinking—assuming the world is a strict competition with winners and losers—is what psychologists and economists call zero-sum thinking.

It’s a powerful mental shortcut. But it often gives us a distorted picture of how the world actually works.


What Is Zero-Sum Thinking?

Zero-sum thinking happens when people treat situations as if any gain by one person automatically means a loss for another.

The term comes from zero-sum games, where the total payoff is fixed. Whatever one person gains, someone else must lose.

A simple example is the game Odds and Evens:

Two players show fingers at the same time. If the total is even, one player wins. If it’s odd, the other player wins. There is always exactly one winner and one loser.


Real-World Examples of True Zero-Sum Games

Some situations really do function this way.

Examples of genuinely zero-sum or near zero-sum situations include:

  • Competitive sports matches
    One team wins, the other loses.

  • Poker and most gambling games
    The money one player wins is money other players lose.

  • Political elections with a single winner
    Votes gained by one candidate reduce the chances of all others.

  • A single job promotion inside a firm
    If one person is promoted, everyone else is not.

  • Limited scholarships or awards
    If one applicant receives the award, another applicant cannot.

  • Draft picks or limited licenses
    When a scarce slot is allocated to one party, others lose access.

In these situations, the “pie” is fixed.


Non-Zero-Sum Situations

In contrast, many real-world situations allow for outcomes where everyone can benefit—or everyone can be harmed together.

A classic illustration is the Prisoner’s Dilemma, where both players can cooperate and both be better off, or both defect and both be worse off.

But non-zero-sum situations are not just theoretical.


Real-World Examples of Non-Zero-Sum Situations

Examples include:

  • Economic growth and trade
    Both sides of a voluntary exchange can become better off.

  • Scientific research and shared knowledge
    One person learning something does not prevent others from learning it.

  • Open-source software and collaborative projects
    Contributions increase the value of the shared system for everyone.

  • Education and skill development
    One person becoming more skilled does not reduce others’ ability to do the same.

  • Public health improvements
    When disease is reduced, everyone benefits simultaneously.

  • Creative collaboration
    Artists, writers, or developers can create outcomes that none could produce alone.

These situations allow for positive-sum outcomes, where the total benefits increase.


Examples of Zero-Sum Thinking in Everyday Life

Despite this, people frequently interpret non-zero-sum situations as if they were zero-sum:

  • Wealth inequality
    “The rich get richer only because the poor get poorer.”

  • Immigration
    “More resources for immigrants means fewer resources for everyone else.”

  • Relationships
    “Loving more than one person means loving each person less.”

  • Skill sets
    “If you have many skills, you must be worse at each one.”

  • Piracy
    “Every pirated download is a lost sale.”

  • Social groups and cliques
    “Stronger identity in one group necessarily weakens all others.”

The problem is not that these claims are always false.

The problem is that zero-sum thinking quietly assumes that only competitive outcomes are possible.


Why Zero-Sum Thinking Is Misleading

Zero-sum thinking collapses complex situations into a single structure:

winner versus loser.

But many real systems allow:

  • mutual success,

  • mutual failure,

  • mixed outcomes,

  • and long-term gains that expand what is available to everyone.

Humans can win together.
They can also lose together.

Yet zero-sum thinking filters those possibilities out of view.


Conclusion

Zero-sum thinking is not wrong because competition does not exist.

It is wrong when it becomes our default way of interpreting the world.

Some parts of life really are zero-sum: elections, promotions, championships, and fixed prizes.
But much of modern society—innovation, trade, education, culture, and cooperation—is fundamentally non-zero-sum.

When we mistakenly treat these domains as if they were rigid contests, we:

  • exaggerate conflict,

  • underestimate cooperation,

  • and overlook opportunities for shared progress.

Learning to recognize when a situation is truly zero-sum—and when it is not—may be one of the most important skills for thinking clearly about politics, economics, relationships, and social life.

Not every gain requires someone else to lose.

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Can Science Tell Us How to Live?

Broadly speaking, there are two main projects for science as it relates to morality:

  1. Explaining human behavior through the evolutionary process

  2. Rationally determine behaviors we should follow or avoid for well-being

These projects should be considered distinct, and we should be careful not to conflate them. Conflating Project 1 and Project 2 risks committing the naturalistic fallacy: just because something is natural does not make it good, and just because something is unnatural does not make it bad.

Social Darwinism, for example, is in no way a moral ideal. But understanding the implications of natural selection is still deeply important for developing a serious science of morality.


Project 1: Evolution and the Roots of Moral Intuition

Let’s look at Project 1 more closely.

Evolution not only provides the basis for the physical structures of organisms, but also the foundations for their behavior. It can therefore provide powerful explanations regarding the origins of moral intuitions, emotions, and values—especially when we compare human behavior with that of other animals.

Before diving deeper into evolutionary explanations, it’s essential to understand something more basic: the material basis of reality and how brains perceive it.

A material reality exists external to the mind. However, we do not perceive this reality directly. What we experience is a model of reality constructed through the filters of our senses.

Consider the famous philosophical thought experiment:

If a tree falls in a forest and no one is around to hear it, does it make a sound?

In a strict sense, the answer is no. Without a conscious perceiver, there is no such thing as sound—only pressure waves moving through air. Sound itself is something brains create.

The same is true for color, taste, and smell. These sensations are not “out there” in the world in the same way matter is. They are experiences produced by nervous systems.

This matters because different organisms—and even different people—experience the same physical reality in radically different ways. Evolution shaped these perceptions because perception drives behavior.

Take the smell of human feces. Why does it smell bad to us?

It’s not because feces inherently stinks, it’s because human brains have evolved to perceive certain chemicals in feces negatively.

These chemicals emanate from feces and become airborne, where they are detected by our nose.

Human feces is a carrier of disease. Organisms that found feces repulsive were less likely to touch it, less likely to become ill, and more likely to survive and reproduce.

But flies experience those same chemicals differently. For them, feces is a food source. What disgusts humans may attract insects.

The important point is this:

Perception shapes behavior, and evolution shapes perception.

From this, we can begin to explain the origins of many moral intuitions. Evolution gives us a “natural morality” rooted in survival and reproduction—but that is not the same thing as an objective morality.


Project 2: Can Science Help Us Decide What We Ought to Do?

Project 2 is more controversial.

Project 1 is descriptive: it tells us what influences human behavior.
Project 2 is normative: it attempts to tell us what we ought to do.

Many critics argue that science cannot answer moral questions. And in one sense, this is correct: science alone cannot invent values from nothing.

However, this criticism often misses the deeper point.

Science may not create moral goals, but once we accept even the most basic moral premise—that suffering is bad and flourishing is good—science becomes highly relevant.

Values are not arbitrary. They are constrained by facts about the well-being of conscious creatures.

In that sense, moral claims can be understood as a specific kind of empirical claim:

X value produces more flourishing and less suffering than Y value.

For example:

Honesty creates more flourishing of conscious creatures than lying.

Often we already have strong intuitions about such claims. But a science of morality would allow us to test them systematically through psychology, neuroscience, economics, and real-world outcomes.

Of course, moral rules are rarely absolute. Honesty may promote flourishing in most cases, but there may be rare contexts where this is not the case.

This is why framing morality as a landscape is valuable:

Our world has many possible outcomes—some better, some worse. Peaks and valleys.

It contains a spectrum of competing values, and the moral question becomes:

Which values reliably move conscious creatures toward the peaks?


The Moral Landscape and the Future of Ethics

There is no doubt that a science of morality is still in its infancy.

Defining “flourishing” is difficult enough—how would we measure it?

  • Wealth?

  • Happiness surveys?

  • Physical health?

  • Brain scans?

  • AI simulations?

Our tools are limited, and the moral landscape is complex.

But early sciences were messy too. Medicine existed long before germ theory, and astronomy existed long before Newton. Progress came through refinement, measurement, and education.

Likewise, the foundations of a science of morality are forming. The purpose is not to replace moral debate, but to ground it more firmly in evidence rather than tradition, dogma, or power.

If morality is ultimately about the experiences of conscious creatures, then understanding those experiences scientifically is not optional.

It is something we ought to continue expanding, progressing, and teaching—like any other science.

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