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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Should Ideas Be Free?

What if ideas, knowledge, and creative works were common property, freely available to all, rather than tightly controlled by copyright and patents? Could a world where intellectual property is openly shared actually foster more innovation, cultural growth, and societal prosperity than our current system?


What Is Piracy?

Piracy is generally defined as the unauthorized use or reproduction of intellectual property:

  • “The unauthorized use of another’s production, invention, or conception, especially in infringement of a copyright.” – Merriam-Webster

  • “The unauthorized reproduction or use of a copyrighted book, recording, television program, patented invention, trademarked product, etc.” – Dictionary.com

A natural question arises: Is piracy equivalent to theft? Theft is almost universally considered morally wrong because it deprives someone of a tangible good. Comparing piracy to theft helps clarify whether similar ethical intuitions apply to digital and creative content.


Rivalrous vs Non-Rivalrous Goods

Humans generally perceive theft as wrong because it deprives someone of a rivalrous good—something that can only be used by one person at a time. Examples include:

  • Durable rivalrous goods: A shovel, which can be used multiple times but only by one person at a time

  • Non-durable rivalrous goods: An apple, which is consumed once and no longer available to others

Some non-tangible goods can also be rivalrous, such as domain names or radio frequencies. Theft almost always involves rivalrous goods, which are scarce by nature.

In contrast, non-rivalrous goods can be consumed by multiple people simultaneously at near-zero marginal cost. Examples include digital music, e-books, broadcast television, scenic views, and clean air. The key question is: Can non-rival goods be stolen in the same sense as physical objects?


Ethical Scenarios of Piracy

Piracy varies in ethical implications depending on how content is consumed, modified, and shared. Here are four concrete scenarios:

  1. Consume copyrighted content without permission for private use

    • Example: Downloading a movie from a torrent site and watching it at home without sharing it.

    • Ethical consideration: Minimal direct harm to others, but creators may lose potential revenue.

  2. Consume copyrighted content without permission and edit it for private use

    • Example: Downloading a song and creating a personal remix or mashup that you never share publicly.

    • Ethical consideration: Often legal under fair use in some jurisdictions. Ethically, the act is private and does not encourage broader free-riding.

  3. Consume copyrighted content without permission, edit it, and share publicly

    • Example: Creating a meme video from a copyrighted film or song and posting it online for others to enjoy.

    • Ethical consideration: Transforming the work adds value, but public sharing could reduce the original creator’s potential revenue and encourage wider copying.

  4. Consume copyrighted content without permission and share publicly

    • Example: Uploading a full copyrighted movie or e-book to a file-sharing site for anyone to download.

    • Ethical consideration: Maximizes potential harm to the creator by widely distributing the work without compensation, creating the most serious free rider problem.

Even when piracy is non-rivalrous, the distribution method and impact on creators influence its ethical evaluation.


Intellectual Property as Common Property

Treating intellectual property as common property could transform the way society creates and shares knowledge. Open access encourages collaboration, accelerates innovation, and allows ideas to propagate more freely. In a world where digital works can be copied at almost zero cost, rigid enforcement of ownership may stifle cultural and scientific progress rather than support it.

At the same time, creators still need incentives to produce high-quality content. The challenge is finding the balance between protecting creators’ rights and maximizing societal benefit through free access and sharing.


Conclusion

Piracy is not morally identical to theft, because most digital content is non-rivalrous. Yet, it is ethically complex: the harm to creators, the potential to encourage innovation, and the way content is shared all matter.

Ultimately, the ethics of piracy force us to rethink what ownership means in the digital age. If society can embrace models that encourage both creation and open access—through fair compensation, voluntary sharing, or commons-based frameworks—we can unlock unprecedented cultural and scientific growth. Piracy, in this sense, is not just a legal problem—it is a moral and societal question about how we value ideas and knowledge.

By approaching intellectual property as a shared resource, we may discover a future where the flow of knowledge benefits both creators and the wider world, creating a more innovative, equitable, and flourishing society.

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The Division of Labor in the Age of AI

One of the defining features of a capitalist economy is the division of labor—breaking work into specialized tasks so that individuals can focus on a narrow set of responsibilities. This system has long delivered dramatic gains in productivity, efficiency, and scale. But the way work is divided is not fixed. Today, artificial intelligence is beginning to reshape specialization itself.

To understand what is changing—and what remains constant—it helps to revisit why the division of labor became so powerful in the first place.


From craftsmanship to mass production

Perhaps the most familiar historical example is Henry Ford and the early automobile industry.

Before Ford, cars were assembled by skilled craftsmen who understood many parts of the production process. Vehicles were expensive and produced in small quantities. Ford’s assembly line radically reorganized work. Each worker performed a narrow, repetitive task and did not need to understand how the entire car was built.

The result was a dramatic increase in output, a steep decline in production costs, and a transformation of the automobile from a luxury good into a mass consumer product. Ford’s famous high wages—his “$5 a day” policy—also helped turn industrial workers into consumers of the goods they produced.

This model became the template for modern industrial capitalism: productivity through specialization.


The philosophical foundations of specialization

The economic logic behind this transformation had been recognized long before factories and assembly lines.

In the eighteenth century, David Hume argued in A Treatise of Human Nature that cooperation and the partition of tasks allow human societies to accomplish far more than individuals acting alone. By combining efforts and dividing roles, collective capacity expands.

A few decades later, Adam Smith made specialization central to his explanation of economic growth in The Wealth of Nations. Smith famously emphasized how dividing production into simple steps could multiply output.

But Smith also warned of a deeper cost. When a person spends an entire working life performing only a few simple operations, their intellectual, social, and civic capacities may atrophy unless education and broader social institutions intervene.

This tension—between efficiency and human development—has been present since the beginning of modern political economy.


Alienation and the human experience of specialized work

A similar concern appears in the work of Karl Marx. Marx argued that highly fragmented and repetitive labor can produce alienation: workers become detached from the product of their labor, from the production process, from their own creative potential, and from one another.

Whether one accepts Marx’s broader critique of capitalism or not, the underlying observation remains relevant. Specialization does not merely organize production—it shapes how people experience work and how they develop as individuals.


Productivity versus human development

The division of labor has generated enormous benefits:

  • faster production and lower costs,
  • large-scale industries and global supply chains,
  • and wider access to goods and services.

At the same time, it can produce important trade-offs:

  • narrower skill development,
  • reduced exposure to the full meaning or purpose of one’s work, and
  • fewer opportunities for creative and integrative thinking.

Historically, the central challenge has been how to preserve the economic advantages of specialization without eroding the intellectual and social capacities of workers.


How artificial intelligence changes the division of labor

Artificial intelligence introduces a new and qualitatively different stage in this evolution.

For most of modern history, the division of labor was about breaking human work into smaller human tasks. AI systems now perform many of those tasks directly—especially tasks involving pattern recognition, classification, translation, scheduling, optimization, and routine decision-making.

As a result, specialization is shifting in at least three important ways.

First, routine cognitive labor is increasingly automated.
Where past automation replaced physical motions on factory floors, AI now replaces standardized mental tasks in offices, finance, logistics, marketing, customer support, and software development.

Second, human work is becoming more supervisory and integrative.
Rather than performing a narrow operational step, workers are increasingly expected to:

  • frame problems,
  • interpret outputs,
  • combine multiple sources of information,
  • and make judgment calls where data alone is insufficient.

Third, the boundary between specialist and generalist is being redefined.
AI tools allow individuals to perform tasks that previously required multiple specialized roles—drafting legal documents, generating code, analyzing data, or producing creative content. In this sense, AI can partially reverse the extreme fragmentation of labor by expanding what a single worker can do.

The division of labor is therefore no longer only about splitting tasks among people. It is increasingly about distributing tasks between humans and machines.


A new version of the old problem

This technological shift does not eliminate the philosophical concerns raised by Hume, Smith, and Marx. It reconfigures them.

On one hand, AI has the potential to relieve workers of the most repetitive and cognitively narrow tasks. In principle, this could free human effort for learning, creativity, social interaction, and higher-level problem solving.

On the other hand, AI can also deepen forms of deskilling if workers are reduced to monitoring systems they do not understand, following algorithmic instructions, or performing fragmented validation tasks at the margins of automated workflows.

The risk is no longer only boredom or repetition. It is loss of agency, reduced understanding of how decisions are made, and diminished capacity to challenge or improve the systems that increasingly shape economic life.


Balancing productivity and human flourishing in an AI economy

The division of labor is not disappearing. It is evolving.

The central challenge remains the same as in earlier industrial transformations, but with new institutional stakes:

  • Education must focus less on narrow procedural skills and more on interpretation, critical reasoning, and systems-level understanding.
  • Workplaces must be designed to keep humans meaningfully involved in judgment, problem framing, and learning.
  • Civic and professional institutions must ensure that workers can understand, question, and participate in the governance of algorithmic systems that affect their livelihoods.

Without these supports, AI-driven specialization may increase productivity while quietly narrowing human development in new ways.


Conclusion

The division of labor lies at the heart of capitalism’s extraordinary capacity to produce, innovate, and scale. From the assembly line to the algorithm, specialization has repeatedly reshaped how societies create wealth.

Artificial intelligence marks the next major transformation in this process. It shifts the division of labor away from ever finer human specialization toward hybrid systems of human and machine work.

The lesson from history is clear. Efficiency alone is not enough.

The greatest strength of capitalism—its ability to reorganize work to generate productivity—must be paired with deliberate attention to education, institutional design, and civic life. Only then can the evolving division of labor in an AI-driven economy enhance not just output and innovation, but also human agency, understanding, and flourishing.

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The Paradox of Free Markets

Free markets are often presented as a simple solution to economic problems. Fewer rules, we are told, mean more competition, lower prices, and greater freedom for everyone.

But this confidence rests on a rarely questioned assumption: that if government steps aside, markets will naturally become—and remain—competitive.

The paradox is that removing public control does not necessarily remove control at all. It often changes who exercises it.


What we usually mean by a “free market”

Modern definitions commonly describe a free market as one without government regulation:

“An economic system in which prices and wages are determined by unrestricted competition between businesses, without government regulation or fear of monopolies.” – Dictionary.com

The idea is straightforward. If businesses are allowed to compete freely and government does not interfere, prices and wages will be set by supply and demand. Markets will discipline bad behavior and reward efficiency.

The deeper assumption, however, is that competition will maintain itself.


Why markets do not reliably regulate themselves

In practice, markets rarely regulate themselves perfectly.

Competition is not a natural or automatic outcome of exchange. It is an institutional condition—one that can weaken when powerful private actors gain control over key parts of the market.

When firms dominate supply chains, distribution networks, digital platforms, or essential infrastructure, they can:

  • restrict entry by new competitors,

  • shape the terms under which others must trade, and

  • influence prices without open rivalry.

A market can therefore be legally unregulated and still be economically constrained.

The absence of government intervention does not guarantee the absence of domination. It may simply shift power from public institutions to private ones.


Modern examples of the paradox in practice

Several of today’s largest and most dynamic markets illustrate how private power can reshape competition even without heavy-handed regulation.

Amazon
Amazon operates the dominant online marketplace in many countries while also selling its own products on that same platform. Independent sellers depend on Amazon for access to customers, pricing tools, logistics, and advertising. This allows Amazon to influence which products succeed and which sellers remain viable.
The market remains “open,” but access to buyers is mediated by a single private gatekeeper.

Google
Google controls the primary gateway to information and online discovery through search. Businesses compete for visibility inside an ecosystem whose rules and rankings are set by one firm.
Even when no formal barriers to entry exist, competitive outcomes depend heavily on platform control rather than direct rivalry between firms.

Apple
Apple controls software distribution on the iPhone through the App Store. Developers must comply with Apple’s technical and commercial rules in order to reach users.
This gives a private firm effective regulatory power over pricing models, business practices, and market access within an entire digital ecosystem.

Live Nation Entertainment and Ticketmaster
Live Nation’s control of major concert venues and Ticketmaster’s control of ticketing infrastructure allow the same firm to influence both live events and how consumers access them.
Here, competition is limited not by public rules, but by private control of distribution and venue access.

In each of these cases, trade and innovation continue. But competition increasingly occurs within privately governed systems, rather than in open markets where rivals meet on neutral ground.


Economic freedom and private power

This is where the paradox becomes clear.

Freedom from government control is not the same as freedom within the market.

Participants may face private forms of control that limit their ability to compete, bargain, or even reach customers. In such cases, exchange still occurs—but it no longer resembles the open, competitive process that the idea of a free market is meant to describe.


The limits of regulation—and the limits of laissez-faire

Recognizing this problem does not imply that all regulation is beneficial.

Poorly designed rules can:

  • raise compliance costs that only large firms can afford,

  • discourage entry by smaller competitors, and

  • entrench incumbents by shielding them from competition.

At the same time, markets are not powerless. In some industries, innovation, technological change, and substitution can weaken dominant positions without direct government intervention.

This leads to an important part of the assessment.

Both extremes are flawed.
Unrestricted markets can allow private power to concentrate.
Poorly designed regulation can lock that power in place.


The assessment: what the paradox actually shows

The real issue is not whether markets should be regulated or unregulated in the abstract.

The central question is whether a market’s institutional framework preserves:

  • open entry,

  • genuine rivalry, and

  • the ability of participants to switch, innovate, and compete.

The paradox of free markets is that competition—the very condition that defines a free market—does not reliably emerge or survive on its own.

A more credible and precise claim, then, is this:

Markets do not reliably protect their own competitive conditions.

When private power becomes concentrated, competitive outcomes can erode just as surely as they can under excessive or poorly designed public control. Under the right institutional design, oversight is not necessarily an enemy of market freedom. It can be one of the conditions that makes meaningful competition—and genuine economic freedom—possible in the first place.

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Songwriting 101: Lovedrug

Today I’m breaking down a song I wrote a couple of years ago called Lovedrug.
This track is a good example of how simplicity in songwriting can be extremely effective when the core ideas are clear.

It uses one of the most common harmonic patterns in popular music:

I – V – vi – IV

(If you haven’t read my earlier post on chord relationships, I recommend starting there for a deeper explanation of why these chords work so well together.)

Let’s jump in.


Key and harmony

Although the song is often described as being in D♯ major, the more standard and readable key name is:

E♭ major

All of the chords in the song are diatonic—they belong naturally to the same key. There are no borrowed or outside chords. This is one of the main reasons the harmony feels so stable and familiar.


Verse progression

E♭ – B♭ – Cm – A♭

Functionally, in E♭ major:

  • E♭ = I

  • B♭ = V

  • Cm = vi

  • A♭ = IV

So the verse follows:

I – V – vi – IV


Chorus progression

E♭ – Cm

Functionally:

  • I – vi

Reducing the harmony in the chorus helps make it feel more focused and emotionally direct compared to the verse.


Making the song easier to play on guitar (capo approach)

These chords are awkward to play in open position. To simplify things, I place a capo on the 8th fret and use open chord shapes.

With the capo on the 8th fret, I use:

Verse shapes

  • G major → sounds as E♭ major

  • D major → sounds as B♭ major

  • E minor → sounds as C minor

  • C major → sounds as A♭ major

Chorus shapes

  • G major → sounds as E♭ major

  • E minor → sounds as C minor

This allows the entire song to be played using familiar open-position shapes while sounding in E♭ major.


Why I chose this key

The reason I chose E♭ major (capo on the 8th fret) is simple:

it fit my voice best.

Choosing a key is often more about the singer than the instrument or the theory. A song can feel completely different when moved up or down by only a few semitones.

During the writing process for Lovedrug, I also experimented with the song in G major and B♭ major before settling on this final key.

Trying different keys is a great way to:

  • find a more comfortable vocal range, and

  • discover new melodic ideas.


Song structure

I would categorize the form of this song as:

A – B – A – B – C – B – B – C

Where:

  • A = verse

  • B = chorus

  • C = instrumental break / interlude

I originally labeled the “C” section as a bridge, but functionally it behaves more like an instrumental interlude. The harmony and overall feel remain very similar to the verse and chorus, and the main difference is that the vocal melody is replaced by a guitar riff.

So the structure is best understood as:

Verse → Chorus → Verse → Chorus → Instrumental break → Chorus → Chorus → Instrumental break


Emphasizing the chorus

My favorite part of Lovedrug is the chorus. Since the melody is especially catchy, I wanted to make it the emotional center of the song.

To do that, I simply made the chorus the most prominent section by:

  • repeating it more than any other part, and

  • placing it close to both the middle and the end of the song.


Lyrics and theme

Here are the lyrics:

Verse

I love to watch you squirm
Every time you lie
Feel the way it hurts

Chorus

You get me high then you bring me down
Don’t know why I ever stick around
Hate to say, hate to say it now
Just a drug, just a drug, just a drug to me

Verse

Why did the fire die?
I want to watch it burn
Every single time

Chorus

You get me high then you bring me down
Don’t know why I ever stick around
Hate to say, hate to say it now
Just a drug, just a drug, just a drug to me


Lyrical idea

The song is about staying in an unhealthy relationship and becoming emotionally addicted to the constant highs and lows.

A line like:

“You get me high then you bring me down”

directly mirrors that emotional cycle. The language is intentionally simple and repetitive to reinforce the feeling of being stuck in a loop.

For this song, clarity and emotional directness mattered more to me than metaphor or complexity.


Melody-writing approach

The melody started with a very simple idea:

  • on the word “high”, the melody walks up the scale,

  • and on the word “down”, the melody walks down the same scale.

After that, I experimented with many variations of this idea until I found one that felt natural and expressive. That version eventually became the verse melody.

From there, I created small variations of it for the rest of the song.

In practice, melody writing is often just:

trying twenty different ways to sing the same short phrase, and keeping the one that feels best.


Final thought

Lovedrug is built almost entirely from:

  • diatonic chords,

  • a very common harmonic pattern (I – V – vi – IV),

  • and a simple melodic idea.

If you take one thing away from this lesson, I hope it’s this:

simplicity can be extremely powerful when the core emotional idea is clear.

It’s very easy to add too much. Sometimes the strongest songwriting move is knowing when to stop adding and let the song breathe.

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