Posts Tagged ‘business’

When the Algorithm Sets the Price

Posted: September 9, 2026 in Anarchy Journal Constitutional, Excess and Algorithms
Tags: , , , , , , , , , , , , , , ,
0

What happens when the price of something stops being a decision and becomes an optimization?

There was a time when pricing was relatively simple.

A store decided what to charge. A landlord decided what to charge for an apartment. A hotel adjusted its rates. A customer looked at the number and decided whether to buy.

The decision might have involved spreadsheets, forecasts, competitors, and plenty of guesswork. Today, software can make that decision continuously.

It can watch demand, inventory, competitor prices, location, timing, browsing behavior, and thousands of other signals. It can adjust the price, watch what happens, and adjust it again.

The question is no longer simply: What should this cost?

It can become: What is the highest price the market—or this particular person—will accept?

That sounds like a technological question. It isn’t. It is an economic one.

Because an algorithm does not decide what to optimize. A person does.

And when the objective is maximizing revenue, the machine can become extraordinarily good at pursuing it.

The Market Learns to Move

Dynamic pricing is not new.

Airlines have used it for decades. Hotels do it. Rideshare companies do it. Retailers have long adjusted prices based on demand, inventory, season, and competition.

There is nothing inherently sinister about responding to changing market conditions. The important change is what software makes possible. A human pricing team might review yesterday’s sales and adjust prices today.

An algorithm can monitor thousands of variables in real time. It can test prices continuously. It can detect patterns no human employee could reasonably track. It can respond before anyone even notices the market has changed.

That is useful when the goal is efficiency. Businesses can manage inventory, respond to demand, reduce waste, and sometimes lower prices. But the same capability works in the other direction.

If a system can identify where demand is strong, it can also identify where consumers have little choice. If it can detect when inventory is scarce, it can detect when a customer is unlikely to walk away. And if it can make those calculations continuously, pricing stops being an occasional judgment.

It becomes an optimization problem. That distinction matters because optimization always depends on the objective.

Efficient at what?

From the Market to You

This is where another form of algorithmic pricing enters the picture.

Dynamic pricing asks: What will the market bear? Personalized pricing asks: What will you bear?

The difference can be subtle. A dynamic system might raise the price of a hotel room because demand increased. A personalized system could use information about the person searching for that room to estimate how much that individual is willing to pay. That information can come from many places: browsing history, location, demographics, purchasing behavior, or other characteristics.

The Federal Trade Commission has been investigating this emerging market under the term surveillance pricing. In 2024, it ordered eight companies offering surveillance-pricing products or services to provide information about how they use consumer data to set or influence prices. The FTC has said these systems can use information such as location, browsing history, demographics, and shopping behavior.

The technology is not hypothetical. The economics aren’t hypothetical either.

A field experiment published by economists Jean-Pierre Dubé and Sanjog Misra found that machine-learning-based personalized pricing could increase firm profits substantially beyond an optimized uniform price. Consumers did not all lose: more than 60 percent received lower prices. But total consumer surplus fell substantially compared with uniform pricing.

That distinction is important. Personalized pricing is not simply a machine programmed to charge everyone more.

It is more sophisticated than that. Some people pay less. Some pay more. The system’s purpose is to identify the difference and capture more of the value.

The old question was: What is the price of this thing? The new question can become: What is this particular person likely to pay?

That changes the relationship between buyer and seller. The price is no longer simply attached to the product. It can become attached to the person.

And that raises a more important question: What happens when the person has no good alternative?

The People Who Can’t Walk Away

For a luxury purchase, pricing power has limits. You can decide not to buy the watch. You can wait for the vacation. You can order something else.

But not every market works that way. People need housing. They need food. They need transportation. They need medicine and childcare.

The fewer alternatives a person has, the less meaningful the choice to simply walk away becomes. That is where algorithmic pricing collides with something older than artificial intelligence: unequal bargaining power.

A corporation may have sophisticated data, pricing software, economists, lawyers, and capital behind a transaction. The person on the other side may have a paycheck and a deadline.

The technology does not create that imbalance. But it can measure and exploit the consequences of it with extraordinary precision. And nowhere is that easier to see than housing.

The Apartment

Consider the case of RealPage. In 2024, the U.S. Department of Justice sued RealPage, alleging that its software helped competing landlords coordinate apartment pricing rather than compete independently. The basic mechanism was strikingly simple.

Landlords provided the software with information about their properties and rental markets, including sensitive information about rents and lease terms. The system processed that information and generated pricing recommendations.

The DOJ alleged that this allowed landlords to use competitors’ nonpublic information to make pricing decisions and reduced the incentive to compete for tenants. The department later expanded the case to include several major landlords.

This matters because there is a major difference between an algorithm responding to a competitive market and an algorithm helping competitors coordinate within that market. The first can make markets move faster. The second can make competition itself weaker. And the issue has not disappeared.

In 2026, the DOJ has continued to reach settlements and proposed judgments involving landlords accused of using RealPage’s system in ways that restricted competition. A proposed September 2026 consent decree involving Pinnacle, for example, would prohibit certain uses of competitors’ sensitive data and certain anticompetitive algorithmic pricing practices.

The machine is not merely calculating the market here. It is becoming part of the market’s structure. That is the important shift.

The algorithm is no longer just a faster calculator sitting behind a human decision. It can influence what competitors charge, how they respond to one another, and what prices consumers encounter.

The tenant does not get a machine. The landlord does. That difference matters.

The Algorithm Doesn’t Need to Hate You

It is tempting to describe all of this as if the algorithm itself were the villain.

That would be a mistake. An algorithm does not hate renters. It does not resent grocery shoppers. It does not want to extract money from anyone. It has no desires. It has an objective.

People choose the objective. That is why saying “the algorithm decided” can obscure more than it explains.

The important questions are:
  • Who built the system?
  • What information does it use?
  • What has it been told to optimize?
  • Who owns it?
  • Who benefits when it succeeds?

The machine does not introduce the incentive. It inherits it.

If a company rewards its pricing system for maximizing revenue, the system will look for revenue. If it rewards the system for increasing occupancy, it will pursue occupancy. If it rewards the system for extracting more from customers who have fewer alternatives, it will become better at finding those customers.

The technology is powerful because it does not have to believe in the objective. It only has to optimize it.

Algorithms didn’t invent exploitation. They industrialized its optimization.

When Optimization Becomes Extraction

This is where algorithmic pricing becomes more than a story about automation.

The system can observe. Then predict. Then price. Then observe what happened. Then learn. Then price again.

That creates a feedback loop. The market provides data. The data improves the model. The model changes the price. The new price changes consumer behavior. That behavior produces more data. And the cycle begins again.

A human pricing department cannot operate at this speed across thousands of products, customers, properties, or markets. A machine can. That gives the owner of the system a new form of economic power.

Not simply the power to set a price. The power to continuously discover how far a price can be pushed.

This is why the question cannot stop at whether algorithms are efficient. Of course they are.

The more important question is: Efficient for whom?

What Happens When the Machines Compete?

There is another problem hiding inside that question.

Competition assumes that sellers have a reason to undercut one another. But what happens when pricing systems become sophisticated enough to learn that constantly undercutting competitors is not the most profitable strategy? Researchers have already explored this possibility.

A 2020 American Economic Review study found that AI pricing algorithms can learn to collude and raise prices without ever talking to each other. More recent economic theory has examined similar problems, including models in which algorithmic pricing can produce inflated outcomes even without the traditional forms of communication antitrust law is designed to detect.

These studies do not prove that every pricing algorithm will collude. They prove something more useful: A machine does not need to be told to collude for competition to become a problem.

If the system is rewarded for profit, it can discover strategies that make more money. Sometimes those strategies may benefit consumers. Sometimes they may benefit the firm at the consumer’s expense. And sometimes they may weaken the competition that was supposed to protect the consumer in the first place.

That brings us back to the question underneath all of this. Who gets the gains from the machine?

Who Benefits From the Optimization?

The answer is not automatically “the corporation.” Businesses can use algorithmic systems to reduce waste, manage inventory, improve forecasting, and operate more efficiently. Those gains can sometimes reach consumers through lower prices or better service.

That is the strongest case for algorithmic pricing. But efficiency does not tell us how the gains are distributed.

The people buying the product generally do not own the pricing system. Renters do not own the housing software. Workers do not own the corporate data. Consumers do not decide what the algorithm is rewarded for maximizing. The companies do. And when the system becomes better at extracting value, the question becomes who captures that additional value.

That is the deeper economic issue. Technology can increase the size of the pie. It does not determine who gets the larger slice.

That depends on ownership, bargaining power, competition, regulation, and the rules surrounding the market.

An algorithm can therefore look like a neutral piece of software while operating inside a very non-neutral economic structure.

The machine may be new. The incentive is not.

The Machine Is Working

There is something almost unsettling about how ordinary this can look.
  • You search for an apartment. A price appears.
  • You look for groceries. A price appears.
  • You book a hotel. A price appears.

You assume the number is simply the price. But behind that number may be a system processing enormous amounts of information, predicting behavior, responding to competitors, and optimizing an objective chosen somewhere above you.

None of this requires a conspiracy. It does not require an evil executive sitting in a dark room. It does not even require anyone involved to believe they are doing something wrong.

The machine can be working exactly as designed. That is the point. The question is not whether the machine works. The question is what we have asked it to work toward.

We built systems capable of measuring human behavior at extraordinary scale. We gave them access to markets, consumer data, competitor information, and real-time feedback. Then we told them to optimize. So they did.

The problem is not that algorithms make decisions instead of humans. It is that we can encode our existing economic incentives into systems capable of pursuing those incentives faster, more precisely, and at a scale no human workforce could match.

Once that happens, pricing is no longer just a number. It becomes a reflection of power.
  • Who owns the data?
  • Who owns the software?
  • Who chooses the objective?
  • Who has the ability to walk away?
  • Who captures the gains?
  • And who pays for the optimization?

Those questions matter because algorithms did not create the economic system they operate inside. They made that system more capable.

Perhaps that is the question we should be asking when the machine sets the price: Was the thing we built it to optimize ever working for us in the first place?

💸 The Trillion Dollar Test 💰

Posted: June 22, 2026 in Excess and Algorithms
Tags: , , , , , , , , , ,
0

A Civilization Measured by What It Tolerates

“We can have democracy in this country, or we can have great wealth concentrated in the hands of a few, but we can’t have both.” — Louis Brandeis

  • Somewhere tonight, a child will go to bed hungry.
  • Somewhere tonight, a family will sleep in a car.
  • Somewhere tonight, someone will drink unsafe water because there is no alternative.
  • Somewhere tonight, a worker will delay medical care because the bill would be too high.

And somewhere in the same world, one man has accumulated a fortune measured in a trillion. That man is Elon Musk.

In the United States alone, nearly 750,000 people experienced homelessness during the most recent federal count. Across the globe, hundreds of millions of people continue to face chronic hunger. Yet at the same time, we have entered an era where an individual can possess wealth greater than the annual economic output of many nations.

I want to be clear from the beginning: I do not believe any human being should possess a trillion dollars. Not Elon Musk. Not the next visionary entrepreneur. Not the most brilliant innovator in history. Not anyone.

This is not because I oppose success. It is not because I oppose innovation. It is not because I believe wealth itself is immoral. It is because a trillion dollars is no longer a measure of success. It is a measure of concentration. A measure of ownership. A measure of power.

And when wealth reaches that scale, the question is no longer what one individual earned. The question becomes what kind of society allows so much wealth to accumulate in one place while so many basic human needs remain unmet.

When Numbers Stop Meaning Anything

Human beings are terrible at understanding very large numbers.

A million dollars sounds enormous. A billion dollars sounds unimaginable. A trillion dollars belongs to an entirely different category.

A million seconds is about eleven days. A billion seconds is about thirty-one years. A trillion seconds is nearly thirty-two thousand years. The scale becomes almost meaningless.

At that point, wealth stops resembling personal prosperity and starts resembling infrastructure. Many governments operate with fewer resources than the fortune now controlled by a single individual. That fact alone should give us pause.

There is a difference between being wealthy and possessing wealth on a civilizational scale.

I have no objection to people becoming rich. I have no objection to people building successful companies. But somewhere between prosperity and a trillion dollars, something changes.

The discussion is no longer about achievement. It becomes a discussion about power.

Every era has a number that reveals what it worships.

  • Ancient empires measured land.
  • Medieval kingdoms measured bloodlines.
  • Industrial societies measured production.
  • Ours measures valuation.

We are told that a trillion dollars is evidence of genius. Perhaps it is. But it is also evidence of something else: a civilization increasingly comfortable with concentrations of wealth and power that previous generations would have considered alarming.

The Lords Return

Defenders of extreme wealth often argue that today’s billionaires earned their fortunes while yesterday’s kings inherited theirs. Fair enough.

But if the outcome is one individual possessing more economic influence than entire nations, the distinction begins to matter less.

  • Medieval kings controlled land.
  • Modern billionaires control platforms.
  • Medieval lords controlled roads, trade routes, and resources.
  • Modern corporations increasingly control the digital roads through which communication, commerce, information, and culture flow.

History spent centuries dismantling hereditary aristocracies because concentrated power was considered dangerous.

Today we celebrate concentrations of power that medieval rulers could scarcely imagine. The lesson of history was never that wealth creation is evil. The lesson was that power concentrated beyond accountability eventually becomes dangerous.

That lesson has not become less relevant simply because the castles have been replaced with data centers.

We’ve Seen This Movie Before

America has already experienced a version of this story. The late nineteenth century produced industrial fortunes so vast that figures like Rockefeller and Carnegie seemed larger than life.

The era became known as the Gilded Age.

  • Economic growth exploded.
  • Innovation accelerated.
  • Industrial output soared.

Yet so did inequality, labor unrest, corruption, and the influence of private wealth over public institutions.

The problem was never that these men built successful enterprises. The problem was the concentration of power that followed. Eventually the public demanded antitrust laws, labor protections, and reforms designed to prevent private fortunes from eclipsing democratic institutions.

The lesson was not that markets are bad. The lesson was that markets left entirely unchecked tend to concentrate wealth and power into fewer and fewer hands.

Today we appear to be relearning that lesson.

The Machine That Makes Billionaires

Elon Musk did not personally build a trillion-dollars worth of products. No human being could.

A trillion-dollar fortune is not created through labor alone. It emerges from ownership.

  • From financial markets.
  • From automation.
  • From intellectual property.
  • From global supply chains.
  • From algorithms.
  • From systems that allow value to compound at extraordinary rates.

This is where economist Thomas Piketty becomes important. Piketty’s research argues that wealth naturally concentrates when returns on capital consistently outpace the growth of the broader economy.

In simple terms, wealth generates more wealth.

  • Ownership attracts more ownership.
  • Capital compounds.
  • The result is not necessarily a conspiracy.
  • It is a tendency.
  • A machine.
  • A system.

Modern capitalism has become remarkably effective at scaling value. What it has not solved is how to prevent that value from concentrating at levels that begin to rival democratic institutions themselves.

The question is not whether Elon Musk worked hard. The question is why modern economic systems repeatedly produce concentrations of wealth that would have been unimaginable to previous generations.

A Civilization’s Report Card

Imagine a society where every child has enough food.

  • Every family has safe housing.
  • Every community has clean drinking water.
  • Every citizen has access to healthcare.
  • Every worker can meet their basic needs.

Now imagine someone becomes a trillionaire.

We could still debate whether that concentration of wealth is healthy. But that is not the world we live in.

The world we live in still contains homelessness.

  • It still contains hunger.
  • It still contains medical debt.
  • It still contains preventable suffering.

These are not mysteries. They are not unsolvable. They are choices.

The scandal is not that poverty exists. Poverty has always existed.

The scandal is that poverty exists alongside unprecedented abundance.

We have solved the problems of production. We have not solved the problems of distribution.

The Question We Avoid

I believe a trillion dollars is a moral failure. Not merely the failure of one individual. The failure of a society. Because every trillion-dollar fortune exists alongside needs that remain unmet.

We are encouraged to marvel at the size of the fortune. Perhaps we should be asking what the existence of that fortune says about everyone who was left behind.

Economist Joseph Stiglitz has spent years warning that extreme inequality is not only unfair but economically inefficient and politically destabilizing. That should concern everyone regardless of ideology.

Extreme poverty creates instability. Extreme concentrations of wealth create instability.

History repeatedly shows that societies become fragile when ordinary people begin to believe the rules only work for the powerful.

The danger is not that one man becomes rich. The danger is that millions conclude the game itself is rigged.

Journalist Glenn Greenwald has often argued that the central political issue of our time is not left versus right but the concentration of power in institutions that become increasingly insulated from public accountability.

The same concern applies here. The question is not whether Elon Musk is a good person. The question is whether any individual should wield economic power on a scale once reserved for states.

What Happens Next?

Many people will celebrate the arrival of the world’s first trillionaire as proof that the system works.

I see something else. I see a warning light.

Not because success should be punished. Not because innovation should be discouraged.

But because no human being should possess that much wealth while so many struggle to obtain necessities.

A trillion dollars is not merely a fortune. It is a concentration of power unprecedented in modern history.

The real story is not Elon Musk. The real story is the world that made a trillionaire possible. A world capable of producing unimaginable abundance while leaving millions behind.

The question is no longer whether we can create trillionaires. The question is why we keep accepting them.

Publicly Traded Private Club

Posted: February 6, 2024 in Excess and Algorithms
Tags: , , , , , , , ,
0

What is the stock market? A central marketplace to buy and sell stock. The NYSE dates back to 1792. It was likely never explicitly stated that it’s a private club. Because of the term publicly traded company, many people probably assume the public interest is being served or that the stock exchange is a public place. It is not.

10% of the populated owns nearly 93% of the stock market. As the late, great George Carlin used to say “it’s a big club, and you ain’t in it”.

10% of the population…wow, and the government gave them how much bailout money in 2008?

I wonder how the public could have benefited from that money? Healthcare, pot hole repair, college debt forgiveness, homeless sheltering, food banks, community gardens, jobs programs, etc. But that’s going into fantasy land. A fantasy where the rich don’t get richer.

Is there a middle ground where the poor don’t have to get poorer?

It is something to see so much data and evidence pile up that America is living in a capitalist controlled oligarchy. The illusion of direct democracy fades for all but the heavily propagandized. Unfortunately the heavily propagandized is still the majority of the civilian population of America.

The evidence of that fact shows up every election season. Red vs blue, republicans vs democrats, neighbor vs neighbor. Tribalism weaponized to keep the unwashed masses fighting amongst themselves rather than looking upwards and their oppressors.

That third parties are still relegated to joke status because the duopoly has people convinced lock, stock, and barrel that they need to vote between the lesser of two evils OR ELSE, is also really something to behold every election season.

How many billions need to be spent on war and corporate welfare each year before something changes? Is there a number? Is there a flash point? Is there a turning point? Do we as a people have it in us?

It is easier to just get by. Take less and be lead. Settle on the sidelines and complain through small talk or comment sections.

However, it is also getting harder to deny reality. Even in a time of compounding misinformation and infinite propaganda, a study that shows that 10% own 93% of the stock market comes out. The same stock market that is used by every mainstream news source as the indicator of whether the economy is doing good or bad for the whole country.

So if 10% are doing good we’re all doing good? No. But every news anchor and every politician in America talks about how good or bad the economy is doing based on how the stock market is doing. So what does it mean if the stock market is doing good, 10% of the population are doing good, the media and politicians all in unison say we are doing good as a whole, but the vast majority are experiencing the toughest times of the past century?

Economic Reality Naiveté

Posted: December 23, 2023 in Anarchy Journal Constitutional, Excess and Algorithms
Tags: , , , , , , ,
0

Naiveté of economic realities is something that seems unfortunately baked into human nature. In America where poor people or people who are just one step above being poor vote against their own better interests for generations.

The “Took Our Jobs” folks who are always ready, willing, and able to point their finger and raise their ire at anyone other than the billionaire capitalist class that is responsible.

…“cash rules everything around me”… is a universally agreed upon law of American life and other first world countries. So what does that mean when for the affluent when applied to income inequality, outsourcing, inflation, and tax cuts?

The people with the least, have the most influence on the lives of others?

Bernays won…in a landslide.

What Does it Mean when #Apple Has More $$$ Than the US Govt?

Posted: October 16, 2014 in Excess and Algorithms
Tags: , , , , , , , , , , , , , , ,
0

eanda logoajclogo2

by @anarchyroll
10/15/2014

It turns out Apple is worth more than a lot of things. A lot of things and a lot of other companies.

The company is valued at over half a trillion dollars and at any one time, has around $160 billion of liquid assets on hand.

The US government for instance, has less than 1/3 of that on hand. Although, as the Forbes article linked above makes sure to note, the US Treasury can at any time print more money and invest it into treasury notes.

What does it mean when a company has more than three times the amount of money as the government  of the country it operates in? Does that tremendous gift on incredible wealth come with added responsibility? A responsibility not just to employees and shareholders, but to cities, cultures, and societies?

Apple hoards so much cash, that Carl Ichan, the man who the lead character in the movie Wall Street is based on, thinks Apple is being too greedy with their profits. That takes a whole lotta greed. Ichan is as ruthless of a capitalist as it gets. If someone who makes his living using money to make money thinks Apple owes something to other people, that puts Apple in a different light than the idolatry bestowed upon their founder and products.

Apple already deserves some scorn for their notorious tax dodging/avoidance practices. They dodge taxes and hoard cash from even their own stockholders. What about the societies that have enabled the company to become richer than governments? What about the roads, schools, bridges, farms, poverty, intelligence, and morale of the places and people Apple has made their billions in? Do they owe something? Should they bear more responsibility to the public than slightly newer, slightly modified consumer electronic gadgets a few times per year?

With great power comes great responsibility. Money equals power in the world we live in. No one person, government, or corporation in the world has more money than Apple. Where does responsibility come in?

 

Older Entries