In August 2019, plant pathologists in Colombia confirmed what the banana industry already knew: TR4 had reached Latin America.

TR4 is a fungus. It attacks the banana vascular systems, stays active in soil for decades, and laughs at current pesticides. It’s a particularly serious threat to the Cavendish banana—the variety that makes up 99% of exported bananas worldwide.

The industry has been here before. In the 1950s, the same disease destroyed a different banana—then the world’s most popular variety, the Gros Michel.

They know exactly how this story ends.

The First Collapse

The Gros Michell banana dominated exports from the 1890s through the 1950s. Unless you’re a senior citizen or very into niche tropical fruit tourism, you’ve probably never seen one. They grew in massive Central American monocultures—thousands of acres of genetically identical plants, all cloned from offshoots. No seeds, no sexual reproduction, no genetic variation.

Then Panama Disease showed up.

The fungus quickly spread farm-to-farm through irrigation water, on trains, and via workers and equipment. Which, if you’re keeping score at home, is basically every way a fungus can spread. Monoculture turned out to be the perfect transmission vector: continuous host density with zero genetic diversity meant the pathogen had an unimpeded highway to span multiple growing regions. And since every plant was a clone of the previous one, each was just as vulnerable as the last.

Now here’s where it gets interesting. Researchers identified disease-resistant banana varieties in 1910. Not 1950, after Panama Disease. 1910. 40 years before it happened. They chose not to diversify.

And if you’re thinking, “Surely they learned from this,” well, buckle up.

What they did instead was abandon infected land, cleared new jungle, and moved operations. Disease followed. They moved again. The political leverage required to keep accessing new land—and the corruption that came with it—is actually where we got the term “banana republic.” It wasn’t always a metaphor; it was once the motivation to overthrow governments and grow more bananas.

By the late 1950s, growers essentially ran out of uninfected land. Gros Michel exports collapsed and cost the industry some $2.7 billion in today’s dollars.

The Replacement

The Cavendish banana had one big thing going for it: complete resistance to Race 1—the specific strain of fungus that killed Gros Michel. Even better, Cavendish could grow in the same infected soils where Gros Michel died. Problem solved, right?

Well… kinda. The industry really just succeeded in replacing one monoculture with another.

Cavendish bananas are also clonally propagated—grown from offshoots, not seeds. Still zero genetic diversity. The Gros Michel plantations just became Cavendish plantations—same regions, same basic cultivation models, different fruit.

There’s a detail buried in the research that should make you nervous, assuming you care about bananas, food security, or dramatic irony. Gros Michel and Cavendish are, genetically, half-siblings. They share an ancestor that contributed two-thirds of their genome. The industry didn’t pivot to something fundamentally different, they pivoted to a close relative that happened to resist that specific strain of pathogen.

Cavendish, remember, was resistant to Race 1. Not to other races of fungus. Not to the new strains that would inevitably pop up over the next few decades.

The industry learned to grow a different banana. They didn’t learn their lesson about monocultures.

They Can’t Change Now

Today, Cavendish comprises 99% of exported bananas. Walk into any supermarket in North America or Europe, check the produce section, and you’re looking at genetic clones of one another in the banana aisle—usually Grand Nain, Williams or Valery, which sound like a law firm but are actually three slightly different flavors of the same genetic mistake.

The entire global supply chain exists to move this cultivar of banana. Modified atmosphere packaging extends shelf life from three weeks to seven. Specialized coatings and packaging materials prevent bruising during shipping. Temperature control systems, ethylene management, forced-ripening centers—every single node in the chain is calibrated for one cultivar, Cavendish.

Retail standards make the problem even worse. Supermarkets demand specific sizes, color curves, and ripening timelines. Bananas that don’t meet spec sell locally, most commonly at farmer’s markets, at steep discounts, if they sell at all. And consumers? They’ve been trained on what a banana looks and tastes like for 70 years now—even though those who have had other cultivars tend to prefer them to Cavendish, which many feel lack sweetness and flavor.

This creates a trap. The structural constraints in these value chains means export-oriented production keeps reinforcing monoculture. The more infrastructure you build for scale and efficiency, the less able you are to accommodate anything else.

Want to diversify? You’d have to rebuild everything from plantations to supermarket shelving. Meanwhile, resistance to diversification and decades of underinvestment mean there aren’t a lot of market-ready alternatives waiting in the wings.

The industry spent decades optimizing for efficiency. They got rigidity instead in a system that works great right up until the moment it spectacularly doesn’t.

History Repeats

TR4 was first spotted in Taiwan in 1967. Since the 1990s, it’s been devastating Cavendish plantations across Asia, Australia, Africa, and the Middle East. For years, the industry tried to contain it with biosecurity protocols, quarantine measures, and constant monitoring.

Then it jumped continents.

Colombia and Peru are major exporters, and both now have TR4. The fungus spreads through contaminated soil, through water, on equipment, or even on workers’ boots as they move between farms. Once it’s established in the ground, it sticks around decades. There’s no practical way to eradicate it from infected land at scale.

The estimated impact is $10 billion in economic damage. The projected job losses run into the hundreds of thousands—real people in regions where banana cultivation is often the primary job provider.

Look, the industry is working on it. Gene editing to make Cavendish TR4-resistant. Breeding programs looking for alternative cultivars. There’s even a GMO variety with resistance genes from wild bananas that’s been approved for use in some countries. That’s right, science is now artificially recreating the genetic diversity that existed for millennia, before science, you know, artificially eliminated it. Progress.

But regulatory approval could take years. Many consumers are wary of GMO food. And even if a resistant variety gets adopted at scale, the fundamental structure doesn’t change—you still have a monoculture, clonal propagation, and infrastructure built around uniformity.

According to research in Phytopathology, the Cavendish era is ending. The question isn’t whether it collapses, but when, and if the industry learned anything this time.

The Lesson They Won’t Learn

The banana industry has now failed the same test twice. Here’s how it goes: monoculture enables scale. Scale demands uniformity. Uniformity eliminates genetic diversity. No diversity equals pathogen vulnerability. Pathogens devastate the crop.

And the industry’s response? Another monoculture. Which is a bit like treating your gambling addiction by switching from blackjack to craps.

Each time through this cycle the trap gets tighter. More infrastructure. Stricter standards. Higher costs to switch.

Think about the timeline. The Gros Michel collapse took decades—slow enough that the industry had time to find Cavendish and scale it up globally. TR4 is moving much faster. Which means the banana industry is on a really tight deadline to make the same mistake a third time. Meanwhile, the infrastructure is more entrenched than it was in the 1950s, making it harder to pivot.

The incentives all point in the wrong direction. Diversifying means accepting lower yields now, rebuilding supply chains from scratch, retraining a consumer on what a banana looks and tastes like, and somehow coordinating all this across thousands of farms in dozens of countries. Maintaining monoculture means you get to keep your existing revenue, write off your sunk costs, and maybe—hopefully—the issue stays manageable long enough to become someone else’s problem.

This isn’t banana-specific. It’s how commodity systems work. You optimize for efficiency, which means cutting redundancy. Cut redundancy, lose your ability to adapt when conditions change.

Bananas are just the most visible example. It’s the one case where you get to watch the same disaster play out twice in one human lifetime.

The industry saw this coming in 1910. They’re seeing it again now. The difference, this time, is that there’s far less jungle to clear.

You’re about to hit the share button. The headline confirms exactly what you’ve been saying for months. Stop. Your bullshit detector just went MIA. Not because you’re gullible—because the content feels right. And that feeling? That’s your bullshit detector being gently tucked in for a nap.

Researchers asked 34,000 people across 16 countries how important it is to only share accurate news. 79% said “very” or “extremely” important. Then the researchers watched what they actually shared. 77% of them proceeded to share at least one false story in a subsequent test.

This isn’t a story about how naive other people are. It’s happening to you too, and probably more often than you realize.

Your Skepticism Works. It’s Just Badly Calibrated

In 1979, researchers at Stanford ran an experiment that should probably be taught at the high school level at every school in America. They gathered 48 people with strong opinions about capital punishment—half in support, half in opposition—and gave everyone two studies to read. One study suggested the death penalty deterred murder. The other study suggested it didn’t.

Here’s the thing: both studies were made up. The researchers pulled the data out of thin air. They were careful to make both equally rigorous and equally flawed—same quality of evidence, same methodological problems, just opposite conclusions.

Both groups rated the study that confirmed their existing view as significantly better conducted. After reading both studies, both sides walked away more convinced they’d been right all along.

This is called motivated reasoning. Your brain selects which cognitive tools to deploy based on whether you want something to be true. When a headline pisses you off, questions come: Who funded this? What’s the sample size? Is this even a real source?

But when a headline confirms something you already believe? Nothing. Crickets. Your brain has left the building.

How Better Reasoning Leads to Worse Conclusions

You might think education fixes this. It doesn’t.

Research shows people fit their perceptions of risk to their values, not to evidence. If your community prizes individual liberty, you’re more likely to dismiss environmental risks because accepting them might mean regulations. If your community values collective action, those same risks feel obvious and urgent, because they reinforce a shared moral logic.

And here’s where it gets really uncomfortable: a 2013 study found that political polarization on contested issues was greatest among people who scored highest on tests of cognitive reflection—those that test the ability to override fast, intuitive thoughts with slower and more deliberate ones. The better you are at reasoning, the better you are at justifying what you already believe. High intelligence doesn’t cure bias. It gives it a podcast, a white paper, and a confident tone.

Meanwhile, false news spreads about 70% faster than accurate news. Not because people are stupid, but because surprising claims feel more important to share.

The information you feel most confident about is often the information you’ve scrutinized least.

Six Questions for Headlines That Feel Right

The good news: research shows that simple interventions work. Prompting people to think about accuracy before they engage with content can reduce false sharing by around 10%.

At scale—hundreds of millions of users—that’s tens of millions of shares that don’t happen. And you don’t need new skills. You already know how to evaluate evidence. You just need to point that ability to content you agree with.

These six questions should help.

1. Would I believe this if I supported the other side?

Take any claim you’re about to share. Now imagine it’s being used to support the position you most despise. If your immediate reaction is “that’s cherry-picked” or “that source is biased” you just identified motivated reasoning in real time.

Remember the Stanford study? Both groups evaluated identical evidence and came to opposite conclusions about the quality of the studies based entirely on whether the results matched what they already believed.

That doesn’t mean both sides of every issue are equally valid. We’re testing whether you’re evaluating the claim or your allegiance to it. If changing the tribal affiliation of the source would change your assessment of its credibility, you’re participating in team sports with footnotes. It’s epistemology-flavored tribalism. All the satisfaction of being right, none of the burden of actually checking.

2. Am I reacting to the claim or how it makes me feel?

Emotional responses happen first. You feel vindication or righteousness, and then your brain goes looking for reasons why that feeling is justified. This happens faster than conscious thought.

There’s another problem: familiarity increases perceived accuracy for both true and false headlines. The more you’ve encountered a claim, the more true it feels. This is how propaganda works. It’s also the blueprint of most social media algorithms.

If your immediate reaction is “finally, someone said it”—be suspicious. That recognition isn’t evidence the claim is true. It’s evidence the claim is compatible with things you already believe, which is precisely when you stop asking questions.

3. Can I explain what would prove this wrong?

Think of a political belief you hold strongly. Now complete this sentence: “I would change my mind if…” Take your time. This is surprisingly difficult.

Can’t do it? Or can you only finish it with something vague like “if there were compelling evidence”?

If you can’t articulate a specific, observable piece of evidence that would change your mind, you’re not holding a falsifiable believe. You’re holding an identity marker. Actual reasoning includes knowing what kind of evidence could prove you wrong.

4. Have I spent more than 10 seconds evaluating this?

About half of false sharing happens because of inattention, not ideology. People aren’t carefully weighing evidence—they’re scrolling, reacting, and sharing. The platform design of social media buries questions about accuracy under engagement metrics.

This is why accuracy prompts work. They don’t teach people anything new. They just shift attention from “will my followers like this?” to “is this actually true?”

Ten seconds is enough time to google the source. To check whether that quote is real. To notice you’re looking at a screenshot of a screenshot of a tweet.

5. Who benefits if I share this?

False news spreads faster than true news partly because people share surprising information to look informed. Being “in the know” has social value completely independent of accuracy. The currency is “seeming informed,” not “being correct.”

The more outrageous the claim, the more share-worthy it becomes. The more perfectly it confirms your bias, the more useful it is for signaling your values.

Research finds that veracity has surprisingly little effect on sharing intentions, despite having a huge effect on judgments of accuracy. You can think something is probably false and still share it because it’s socially useful.

6. Am I confusing “this sounds true” with “this is true”?

When information aligns with your preferences, you subject it to less scrutiny. When it contradicts them, you hold it to impossible standards. And the same evidence that might move you toward an unwelcome conclusion will instead strengthen your original belief if that belief is important to your identity.

If you’re absolutely certain about something you learned from a headline—if even considering doubt feels like betrayal—that’s the moment to be most suspicious. Not of the claim necessarily. Of your own certainty.

The stronger you wish that something is true, the higher your standard of evidence should be.

The Part Where This Still Goes Wrong

Let’s be clear: these questions have limits. A framework that’s been circulating in journalism school for decades isn’t a guarantee of accuracy.

These interventions are less effective when identity protection is the primary motivation. Even knowing about motivated reasoning doesn’t make you immune to it. You can read this entire essay, nod along, and still share the next headline that confirms your beliefs. In that moment it never feels like bias. It feels like spotting something that’s plainly, undeniably true.

The goal isn’t perfection; it’s catching yourself slightly more often. Taking a bite of the elephant.

Why the Share Button Rewards the Wrong Instincts

Here’s what’s actually happening when you share something: you’re not making a statement about truth, you’re making a statement about loyalty.

Research shows a stark disconnect between what people judge to be accurate and what they choose to share. You can know something is questionable and share it anyway because it signals the right values. The content you amplify says less about your relationship with truth and more about what you’re protecting. Studies across 16 countries, remember, found the same pattern of people failing to consider accuracy. Not because they can’t, but because platform design makes other things more salient—likes, comments, shares.

The intervention is simple in theory and excruciating in practice: redirect the skepticism you already have. You know how to evaluate sources. You know how to spot logical fallacies. You do all of this constantly, relentlessly, to claims you dislike.

Now do it to the ones you like, too.

Especially to the ones you like.

The headlines that feel most right—the ones that perfectly capture what you’ve been saying—those are the ones that need the most scrutiny. Not because they’re more likely to be false. But because that feeling of rightness is your brain saying “this protects something important.” And when protection becomes the goal, truth-seeking becomes optional.

Before you share something that feels like vindication—pause. Ten seconds. Six questions.

The most dangerous information, after all, isn’t what you disagree with. It’s what you never thought to question.

The drugs didn’t get worse. The sugar pills got better.

A 2015 meta-analysis published in the journal “Pain” looked at 84 clinical trials of neuropathic pain drugs from 1990 to 2013. Placebo responses climbed steadily, reaching an average 30% reduction in pain by the end of the study period. Drug responses stayed flat.

And this is only happening in one country. European trials showed no increase. Asian trials showed no increase. Only American ones did. American exceptionalism has conquered many frontiers. But this is a new one.

The Sugar Pill That Ate Clinical Research

FDA drug approval works like this. You give some patients the real drug, others get an inert pill, you measure the difference. If the drug wins by a statistically significant margin, it gets approved. If it doesn’t, it’s back to the drawing board. This is an oversimplification, but when discussing placebos, this is really the nuts and bolts of it all. And Congress codified all of this into law in 1962.

One assumption holds the whole thing together: The placebo arm is stable. The sugar pill is fixed. Known. You’re measuring the real drug against a constant.

In this case, though, the constant is moving. Researchers estimate that more than 90% of potential drugs for neuropathic and cancer pain have failed in advanced clinical trials over the past decade. Not because the drugs don’t reduce pain, but because they can’t reduce it enough beyond what the placebo already delivers.

Defenders of the current system argue, reasonably, that any drug that can’t beat a sugar pill shouldn’t be on the market. The problem isn’t the standard. The problem is that the sugar pill keeps getting better, and only in the country that spends more than any other convincing its citizens that pills work. The U.S. has, in effect, built the world’s most expensive placebo.

Ask Your Doctor if Advertising Is Right for You

The United States and New Zealand are the only two countries on earth that allow pharmaceutical companies to advertise prescription drugs directly to consumers. Every other country has banned the practice.

In 1997, the FDA loosened its disclosure rules for television ads, allowing pharma companies to mention only “major risks” instead of a full list of side effects. Direct-to-consumer (DTC) ad spending climbed from $1.3 billion in 1996 to $6 billion by 2016. The FDA’s own enforcement went the other direction: Violation letters dropped from more than 130 a year in the late 1990s to three in 2023.

What did $6 billion a year buy? Americans now use prescription drugs at higher rates than a generation ago. Physicians are 17 times more likely to prescribe a drug a patient requests by name. And France and Canada’s independent drug assessment systems rated 68% of top-selling U.S. drugs “low added benefit.”

To recap: Americans see the ads, ask their doctors for the drugs, doctors write the script, and most of the drugs aren’t particularly all that good for what they’re trying to treat. The system works perfectly, if you’re the one selling the ads.

On the mechanical side, U.S. trials also got bigger during that timeframe. In 1990, the average neuropathic pain trial enrolled fewer than 50 patients and lasted four weeks. By 2013, that number had grown to more than 700 patients over 12 weeks.

European and Asian trials didn’t show the same inflation. Longer trials with more patients, more touch points with nurses and clinicians, and more clinical staging all track with larger placebo responses. Canadian neuroscientist and Professor of Pain Studies Jeffrey Mogil put it plainly: “The data suggest that longer and larger trials are associated with bigger placebo responses.” It’s a theory you can’t test, about a problem you can’t measure, in the only country where both are true.

Put those two trends together. The only country where placebo responses are climbing is also the only market where pharmaceutical companies spend billions a year telling consumers drugs will make them feel better. The team behind the Canadian assessment outline DTC advertising as one plausible explanation, while noting it can’t be tested directly with their data. (New Zealand, the only other DTC country, has too small a clinical trial market to test the hypothesis.)

How to Unbuild a Placebo

So the placebo problem is getting worse, and it’s concentrated in the United States. What is the industry doing about it?

Not asking why Americans believe in pills so much. Instead, they’re attempting to engineer that belief out of clinical trials.

Researchers developed something called the Placebo-Control Reminder Script, or PCRS: a coached speech read to clinical trial participants before the trial begins, designed to discourage them from reporting benefits if they received a placebo. This is a real thing that real scientists developed and published in a real journal, which is a sentence that shouldn’t need saying, but here we are.

The researchers’ goal was measurement accuracy, not manipulation. But look at the result: They ran an all-placebo trial, published in “Neuropsychopharmacology.” Every participant got a sugar pill. Half got the coaching script. Half didn’t. The coached group reported less improvement in their depression symptoms.

The coaching... worked, I guess? It made people report feeling less better.

Here’s the other approach. Research on the genetics of placebo responses found that a gene variant is linked to how strongly a person responds to a placebo. People who carry two copies of the variant have three to four times higher dopamine in the prefrontal cortex and respond to placebos at far higher rates. That’s about a quarter of the population. One in four humans is, from the pharma industry’s perspective, too hopeful to be used in a study.

A biotech startup called Biometheus began developing genetic tests to identify these individuals and exclude them from drug trials before they could “ruin” the data with their inconvenient biology.

The science is real, though still early stage: A 2018 study found that previously identified placebo-associated gene variants did not predict outcomes in a Phase III inflammatory disease trial. A Pfizer executive told the Washington Post in 2016 that a usable genetic screen was “still a few years off.” That was a decade ago.

To sum, the pharmaceutical industry built a $6 billion-a-year advertising machine that taught Americans to believe in pills. The placebo response rose. And the industry’s answer is coaching scripts that train people not to feel better and genetic tests that screen out people whose biology responds too strongly to hope.

Faith at the Pharmacy, Skepticism in the Lab

The pharmaceutical industry spends $6 to $8 billion a year on DTC advertising to build consumer belief in the drugs it markets. That belief drives interest, which leads to prescriptions, which drives revenue, which funds the next round of advertising.

Then the same industry spends $1 to $2 billion per drug on clinical trials trying to prove those drugs work. And the primary obstacle to proving they work, at least in the U.S., is the belief that the advertising created. The sugar pill cohort keeps getting stronger because the patients in that cohort are Americans who’ve been watching pharmaceutical commercials since 1997, who walk into a clinical trial already half-treated by three decades of consumer marketing.

The industry wants faith in the pharmacy, but skepticism in the lab. It wants consumers who believe in medicine and trial patients who don’t. It’s spending billions on both sides of that contradiction. The left hand spends billions building belief. The right hand spends billions burning it down. Both hands bill by the hour.

This isn’t just a pain drug problem. It shows up in antidepressants too. The average drug-placebo gap on the Hamilton Depression Rating Scale shrank from six points in 1982 to three points by 2008. For most approved antidepressants, fewer than half of the efficacy trials submitted to the FDA found the drug superior to placebo. Not most trials. Not a majority. Fewer than half. If you ever wanted proof that marketing works, here it is: it works so well it broke the science.

Chronic pain. Depression. Anxiety. The conditions where placebos hit hardest are the ones where patients suffer most and have the fewest alternatives. Where your expectation of relief can look like actual relief on a clinical scale. Rising placebo response doesn’t slow down drugs for conditions you can measure with a blood draw or a scan. It slows down drugs for the people whose suffering is the hardest to measure and easiest to dismiss.

Every failed trial adds years to a pipeline that already takes 10 to 15 years from discovery to approval. The people waiting at the end of that pipeline are real, and their pain is not a measurement artifact.

The control isn’t controlled. The baseline is culturally contaminated. Drugs that would have cleared the bar in 1996 fail today in the U.S. but would still pass in Europe, where patients haven’t spent three decades marinating in pharmaceutical commercials.

The industry’s response is not to reconsider the advertising. It’s to coach the hope out. Screen the believers. Redesign the trial. Do everything except turn off the television.

You find tickets to a show you’ve been waiting months to see. Face value: $85. You click through to checkout. Total: $112. That’s a 32% markup for the privilege of paying for concert tickets with your own money.

Service fee. Facility charge. Order processing fee.

The holy trinity of “go fuck yourself.” You pause. You curse. You pay anyway.

You already know the Ticketmaster story. The fees, the frustration, the ritual of outrage. This isn’t another entry in that genre. It’s about why directing all that anger at Ticketmaster alone lets the real decision-makers off the hook.

Two former Ticketmaster CEOs have publicly admitted it. The company was designed, from the ground up, to absorb hatred on behalf of venues, promoters, and artists. Your rage is directed at a company paid to be the bad guy.

Once you see how this arrangement actually works, you’ll understand why yelling at Ticketmaster changes nothing.

Misdirection

Ticketmaster is a greedy monopoly that exploits its market dominance to charge outrageous fees in the name of convenience. It pockets the money, laughs at customers, and faces no consequences because there’s no real competition.

All true. Mostly, anyway.

Here’s what everyone yelling at Ticketmaster misses though. The company keeps a small fraction of those fees. The venues take most of it. Ticketmaster is paid to make sure you never figure that out.

In 2009, Irving Azoff—then CEO of Ticketmaster—testified before Congress. What he said should have ended the “Ticketmaster is evil” discourse forever, replacing it with something more accurate.

“Ticketmaster was set up as a system where they took the heat for everybody. In that service charge are the credit card fees, the rebates to buildings, rebates sometimes to artists, sometimes to promoters. We’re like the IRS. We deliver the bad news,” Azoff testified.

Comparing yourself to the IRS is a choice. Doing it in front of Congress is a flex.

This wasn’t a slip. It was sworn Congressional testimony. Azoff wasn’t apologizing; he was explaining the business model to lawmakers who didn’t seem to understand it. (They still don’t.)

Fourteen years later, Fred Rosen—the CEO who built Ticketmaster into a juggernaut between 1982 and 1998—gave an interview to CBC Radio. He offered an identical explanation to Azoff. Rosen said, “I [made] it so that Ticketmaster would take the hit for everyone, but Ticketmaster never got all the money.”

When pressed on whether this was fair to consumers, Rosen didn’t blink: “Do you not understand this is a business?”A sentence that has never once preceded good news for the person hearing it.

Two CEOs. Fourteen years apart. Same admission.

Eric Budish, an economist at the University of Chicago who studies ticket markets, told the Seattle Times: “Ticketmaster is effectively paid to be a punching bag.

Follow the Money

So where does the money go? On a $100 ticket with $27 in fees—about average, according to the US Government Accountability Office—here’s the breakdown, per Ticketmaster’s own disclosures:

YOUR $100 TICKET → $127 AT CHECKOUT

Where the $27 goes:

$12-15 → Venue (facility fee + their cut of service fee)

$5-7 → Ticketmaster (their actual take)

$2-3 → Credit card processing

$2-5 → Artists/promoters (when they’ve negotiated for it)

WHO GETS THE MOST: The venue

WHO GETS BLAMED: Ticketmaster

Ticketmaster, notably, does not include this chart on its website.

The venue receives “around two-thirds of the service charge.” Ticketmaster keeps “about 5% of the all-in price.” On a $127 ticket, that’s roughly $6.

This arrangement has existed for decades. What’s changed is the scale.

In 1977, Ticketmaster’s first show—ELO in Phoenix—featured a $6.50 ticket with a $0.25 service charge. Four percent. Today: 27%.

That’s not inflation. That’s a 575% increase in the fee-to-ticket ratio because venues wanted more, promoters wanted a cut, and eventually artists started negotiating for a share too. Ticketmaster got bigger fees to split. Everyone won. Well, except you.

Economists call this extraction. Everyone else calls it getting mugged, but politely.

The Psychology of Getting Played

There’s a reason Ticketmaster shows you an $85 ticket and hit you with a $112 charge at checkout instead of just listing the all-in fee from the start. It’s called drip pricing. It’s well-studied. And it works.

A 2020 study in Marketing Science found that consumers who see fees early in the checkout process choose cheaper-looking options, even when the total cost ends up higher. You’ve already invested the time. You assume everyone else charges the same. You click “purchase.”

A separate study confirmed exactly what you’d expect. “With drip pricing, consumer surplus is lower, and seller profits are higher.”

The FTC, in its December 2024 rule on junk fees, called drip pricing “the quintessential example of bait-and-switch pricing,” and then proceeded to do nothing about it. Bait-and-switch pricing is illegal. Drip pricing? Fair game.

Look, I just report on nonsense, I don’t make it up.

It (Somehow) Gets Worse

Live Nation—Ticketmaster’s parent company since 2010—controls roughly 80% of primary ticketing at major concert venues. It promotes 60% of all major concerts. It operates 265+ venues. It manages 400+ artists. The technical term is “monopoly,” a market condition where the shrug is built in.

The DOJ sued Live Nation in 2024. 40 state attorneys generals joined. Attorney General Merrick Garland declared: “It is time to break it up.” The lawsuit is ongoing.

And that’s great. Except, it won’t really fix anything.

Live Nation’s defense is partially correct. In its response to the lawsuit, the company pointed out that “the bulk of service fees go to venues” and that artists, not Ticketmaster, set face value.

Industry analysts confirmed. Analyst Bob Lefsetz, wrote: “The money’s just going to be shifted around, but it will all be behind the scenes and you’ll end up paying the same.”

The problem isn’t just Ticketmaster. It’s the arrangement. Everyone benefits except you. And yes, that includes the artists who cry foul and then jack up ticket prices behind-the-scenes.

Robert Smith, frontman of The Cure, made headlines in 2023 when he negotiated partial refunds for fans after fees on $20 tickets exceeded the ticket price. He rejected dynamic pricing. He demanded lower fees. He got Ticketmaster to comply.

Most artists don’t.

In a 2024 interview, Smith explained why:

“It was easy to set ticket prices, but you need to be pig-headed. We didn’t allow dynamic pricing because it’s a scam that would disappear if every artist said, ‘I don’t want that!’ But most artists hide behind management. ‘Oh, we didn’t know,’ they say. They all know. If they say they do not, they’re either fucking stupid or lying. It’s just driven by greed.”

The bad cop arrangement doesn’t just protect venues. It protects artists too.

The New Rule (And Why It Won’t Help)

On May 12, 2025, the FTC’s junk fees rule took effect. Ticket sellers now have to display the total price upfront. No more hiding fees until checkout.

The rule doesn’t cap fees. Doesn’t ban any fee types. Doesn’t touch dynamic pricing.

Ticketmaster supported the rule. This is like a casino supporting better lighting. The house edge stays the same, but at least you can see how much you’re losing.

Experts are skeptical that it’ll matter. Andrew Mall, a music industry professor at Northeastern, told CNBC: “If there are any consumers who have been expecting fewer fees as a result, they will be disappointed.”

You’ll see the full ticket price now.

Prices will stay the same.

Progress.

Now What?

Boycotting Ticketmaster is functionally impossible. Major venues have exclusive contracts lasting a decade or more. Ticketmaster’s competitors charge similar fees anyway. (The Hustle found SeatGeek averaged 37% fees, actually higher than Ticketmaster.) But this is what choice looks like when all the choices are bad.

Buying at the box office sometimes reduces service fees. But facility charges still apply. And many venues don’t have box offices anymore.

What might actually work:

• Complain to venues, not Ticketmaster. The venue sets the fee structure. The venue keeps most of the money. They should hear from you directly about exorbitant service charges for the privilege of entering and buying exorbitantly-priced beer.

• Ask artists about their fee arrangements. Robert Smith proved artists can negotiate lower fees if they’re willing to fight for them. Most aren’t. Public pressure might change that. Social media is a tool.

• Support the DOJ case. A 2023 poll found 60% of Americans support breaking up Live Nation—72% of Democrats, 50% of Republicans. Even if it doesn’t lower fees immediately, it may create market space for alternatives.

• Push for state-level all-in pricing laws. Connecticut, New York, and Tennessee already have them. More states are considering them.

Ticketmaster isn’t the cause of high ticket prices. It’s the infrastructure that makes them feel unavoidable. By concentrating consumer anger on a single, highly visible company, the rest of the industry gets to quietly agree on outcomes without ever owning them publicly.

Artists can shrug, venues can plead necessity, promoters can point elsewhere, and everyone can say their hands were tied. That story holds until you follow the incentives instead of the headlines.

Once you do, it becomes clear that Ticketmaster isn’t pulling the strings. It’s just standing exactly where the strings need it to stand.

The optimal credit card utilization rate—the percentage that maximizes your credit score—is between 1% and 10%, and no more than 30%, ever.

Not zero percent. Not paid off completely. Slightly in debt. On purpose.

Ignore your credit entirely and your score suffers. Lean on it too hard and you’re labeled a risk. Maintain a balance of $100 on a $10,000 credit line? Chef’s kiss. Perfect. You need debt to prove you don’t need debt, which is exactly as nonsensical as it sounds. It’s like needing 10 years’ experience to get an entry-level job, except the entry-level job costs you 28% APR.

Can Arkali, a FICO executive, actually admitted this in an interview: there is nothing “optimal or significant” about that 30% utilization threshold everyone obsesses over. Nothing. No research backing it up. No mathematical justification. Just a number that became conventional wisdom through repetition. The entire system is built around using credit you don’t need, in amounts that have no empirical basis, to prove you’re responsible with money.

We’re all performing in a very expensive play, and nobody’s quite sure who wrote the script.

Credit Scores Don’t Measure Responsibility. They Measure Compliance.

Your FICO score breaks down like this:

• Payment history: 35%

• Credit utilization: 30%

• Length of credit history: 15%

• Credit mix: 10%

• New credit inquiries: 10%

The average US score is currently 715, and 90% of lenders use FICO to decide whether you get approved. It’s the standard. The only standard that matters, really.

Now here’s what’s not in the calculation: your income, your savings, your investments, or whether you’ve paid rent on time for the last decade.

I’ll give you a second with that info.

The Federal Reserve found a “moderate positive correlation” between income and credit scores, sure—people with more money tend to have higher scores—but income itself isn’t a variable in the algorithm. You could have $100,000 in savings, a stable job pulling six figures, and zero debt. The current scoring system looks at this and responds the way a DMV clerk responds to joy: it doesn’t.

No credit history means no score. No score means no mortgage, probably no apartment, maybe even no job (depending on the employer). You’re Schrödinger’s borrower: simultaneously too risky to lend to and too responsible to have a score.

And rent? The thing that proves you can make a large payment on time, every single month, without fail? Only 13% of renters had it reported to credit bureaus in 2025. The other 87% could be paying thousands monthly, for years, and it’s completely invisible to the system. The Urban Institute found that rent reporting can boost scores and visibility, but landlords have no real incentive to report good news about tenants.

The Consumer Financial Protection Bureau states: 7 million Americans—2.7% of adults—are “credit invisible.” They have no credit file at all. It’s likely a significant portion of those would benefit from something as simple as adding rent, or even utility payments to the credit scoring system.

If credit scores aren’t measuring our ability to make timely payments, what exactly are they measuring then? Well, it’s not “Are you financially responsible?” so much as “Are you borrowing in a way that’s profitable for lenders?”

And those are two very different questions.

The Goldilocks Debt Zone

Credit utilization accounts for 30% of your FICO score. Everyone says to stay under 30% utilization, but if we look at the actual data: people with scores of 720 or higher average a 10.2% utilization. People with scores of 579 or below? They’re averaging 75.7%.

Let’s translate. If you have a $10,000 credit limit:

• Pay it off completely, every month? Bad. The algorithm punishes you.

• Max it out? Terrible. Obviously irresponsible.

• Keep a small balance between $100 and $1,000? Perfect. Gold star. A+ credit behavior.

The sweet spot is being perpetually, slightly broke. On purpose. You have to keep yourself in a little bit of debt, but not too much, or too little. The Goldilocks debt zone.

It gets better. Another 10% of your score comes from something called “credit mix.” This is the algorithm rewarding you for having more types of debt. Just a mortgage? That’s fine, but not great. Mortgage plus a car loan plus a credit card? Now we’re talking. The system wants you to diversify your debt portfolio like an investment strategy, except you’re investing in owing money to different institutions, which is—double checks notes—not a great investment strategy.

Warren Buffett famously said “diversification is protection against ignorance.” The credit scoring system said “yes, exactly, now go get ignorant across multiple loan types.”

Let’s say you pay off your car loan. You’ve been making smart financial decisions, living within your means, not taking on unnecessary debt. This should be a win. In practice, it often lowers your credit score. Why? Because the system isn’t measuring responsibility so much as participation. Close an account, reduce your monthly payments, and you’ve given it less to track. The math changes. The judgment follows.

Credit Scores Don’t Just Measure Risk. They Create It.

Lower-income households aren’t just poorer. Their income is unstable. Hours vanish at work. Bills arrive uninvited. Cars break when they’re needed most. And there’s usually no savings cushion to soften the blow. Most Americans don’t even have $1,000 set aside for emergencies.

So here’s what happens in periods of emergency. A kid gets sick. Overtime hours aren’t available at work. You miss a credit card payment. Your score drops, sometimes dramatically. Your interest rates go up on everything. Now everything costs more precisely when you can least afford it. Higher-income borrowers can absorb the same mistakes. Same circumstance. Same late payment. Minor consequences.

Federal Reserve research shows that credit outcomes correlate strongly with income and neighborhood, even when payment behavior is identical. Where you live changes what that late payment means for your future. They call it “risk-based pricing.” You could also call it kicking people while they’re down, but algorithmically. And this isn’t a bug in the system—it is the system. It’s working exactly as designed.

The numbers are getting worse, too. FICO’s Fall 2025 report shows that the middle score range—600 to 749 FICO scores—shrank from 38.1% of Americans in 2021 down to 33.8% in 2025. People are moving to the extremes. The middle is disappearing. Gen Z—the youngest and least likely to absorb major financial blows—got hit the hardest with the biggest year-over-year drops.

We also saw evidence of this when student loan payments resumed after the pandemic pause. 6.1 million student loan holders (of 42 million) had delinquencies added to their reports between October 2023, when payments resumed, and May 2025 when lenders reinstated collection activities (including derogatory marks on your credit report).

The median score for low-income Americans sits at 658, in the “fair” range. Middle and high-income Americans, conversely, sit in the “very good” range of 740-799, on average. That gap translates to higher interest rates, larger security deposits, fewer housing options, and sometimes just flat-out rejection for loans, housing, or employment.

As it turns out, a system designed to assess risk ends up creating more risk for people who are already struggling. It is, indeed, more expensive to be poor.

The System Isn’t Broken. It’s Optimized For The Wrong Thing.

Viable alternatives already exist. We’re just not using them.

• Rent and utility payment history. HUD research shows positive rent reporting leads to an average 23-point increase in credit scores. The data exists. The infrastructure exists. We’re just not prioritizing it.

• Cash-flow underwriting. Some lenders are already doing this: analyzing your income and expenses instead of basing underwriting decisions mostly on FICO scores. Turns out it’s more accurate and more predictive of whether you’ll actually repay the loan. Wild.

• Manual underwriting. Actual human beings evaluating the full financial picture. This is what we did for decades. We could do it again, especially for major purchase decisions like mortgages.

• Limiting where scores get used. Stop using them for employment screening. Stop using them for insurance rates. Stop using them to decide if someone can rent an apartment. None of these uses have anything to do with creditworthiness; they’re convenient forms of discrimination that look objective because there’s a number attached. (Remember, credit scores predict debt-repayment behavior, not bill-paying behavior.)

Other countries are ahead of the curve and doing this already.

• In the Netherlands, the BKR system is a national registry that records debts and arrears—it focuses on whether your obligations are manageable, not on generating a universal score that follows you everywhere.

• In Japan, multiple bureaus provide credit files, but lenders emphasize employment stability and income over scores when making decisions.

• Germany has SCHUFA, which provides credit reports, but housing decisions emphasize income verification and rental history alongside collected debt information.

These countries managed to avoid turning financial history into a permanent social credit score. We could learn something. We won’t, but we could.

How We Automated Human Bias

Credit scoring became widespread in the mid-20th century to standardize risk assessment. The explicit goal was replacing human judgment—which led to bias, especially in terms of race and gender—with automated systems that would be objective and fair.

And we did that. Kinda. We replaced human judgment with differently-biased automated systems. The difference is that discrimination by algorithm looks fair, clearer on paper, and free from bias. Math can’t be biased, right? It’s just numbers. Just data. Just… encoding the exact same structural inequalities that prejudiced loan officers once did, except now with a veneer of objectivity that makes it harder to challenge.

The current system was designed to optimize two things: risk tolerance and profitability. Not fairness. Not social mobility. Not economic justice. Risk and profit.

And it does those things incredibly well.

That’s why it won’t change. We have the tools. We have the data. We have working alternatives. The obstacle isn’t technical, it’s incentive. The institutions that built this system still benefit from it. Why would they change it?

The Real Purpose of a Credit Score

A credit score is not a moral judgment. It’s not a measurement of character. It’s not even measuring your financial responsibility. It’s a profitability estimate dressed up as a measure of trust to repay debt.

When a system rewards debt, penalizes financial stability, and treats caution as risk, the problem isn’t the people trying to navigate it. The problem is the system.

Remember the paradox: to build good credit, you have to use credit. But to use it responsibly, by the rules of the scoring system, you actually have to behave a little irresponsibly. The highest credit scores go to people who deliberately carry small balances, keeping themselves just short of paid-off, in amounts that are carefully optimized by an algorithm with no real connection to financial health.

We should probably stop pretending this is objective just because it comes with decimals. Math doesn’t make a system fair. It just makes it harder to argue with at a dinner party. But try explaining to someone that their credit score dropped because they paid off their car, and watch their face go through all five stages of grief in real time.

A thermometer tells you if it’s cold. It doesn’t decide whether you get a roof. Credit scores do. And somehow we’ve all agreed that it’s normal.

In March 2025, a group of JP Morgan Chase employees sued their own employer. The claim: JPMorgan’s health plan, managed by CVS Caremark, charged them $6,229 for a 30-unit supply of generic teriflunomide, a multiple sclerosis drug.

The same drug, same quality, at ShopRite: $29.24. At Mark Cuban’s Cost Plus Drugs: $11.05.

That’s a 200x markup at the pharmacy counter and a 564x markup over the online price. Same pill. Same milligram. Same disease. The complain notes that JPMorgan pays Caremark roughly $3 million a year in administrative fees, plus “many millions more” in fees Caremark collects through the pricing itself. Which is a bit like paying a locksmith to change your deadbolt only for him to sell your house key on the side.

JPMorgan is the largest bank in the United States. $240 billion in annual revenue. An army of actuaries whose entire job is finding inefficiencies in exactly this kind of spending. If they couldn’t see it, what chance does the HR department at your company have?

A similar lawsuit against Jonson & Johnson’s health plan turned up the same pattern: Express Scripts charged J&J employees $10,200 for a 90-day supply of teriflunomide that Wegmans—a grocery store chain—sold for $40.55. The complain even included a line you don’t normally see in court filings: “$10,200, which is not a typo.”

You’ve probably heard that a pharmacy benefits manager (PBM) exists to negotiate your drug prices down. That is the single most expensive lie in American health care.

The Tollbooth Between You and Your Medication

Here’s what PBMs say the job is: negotiate drug prices, build lists of covered medications, process claims, and save everyone money. The PBM trade group claims the industry cuts drug costs by 40% to 50% and delivers $10 in savings for every $1 spent. PBMs manage prescriptions for 289 million Americans. The pitch is simple. The PBM stands between you and Big Pharma, on your side. This is technically true in the same way that a tollbooth stands between you and the highway.

Here’s what PBMs actually do.

Three PBMs (CVS Caremark, Express Scripts, and OptumRX) handle about 80% of all prescriptions filled at U.S. retail pharmacies. A 2024 Journal of the American Medicine Association (JAMA) study analyzed 14 billion prescriptions across 91 PBMs and found a market so concentrated it blows past the Department of Justice’s own threshold. Three companies. Eighty percent of the market. A cartel with better PR.

But concentration is only half the story. PBMs are supposed to be neutral middlemen. Middlemen that decide which drugs are covered, which pharmacies patients can use, and how much everyone gets paid.

The three largest PBMs are not neutral. Each sits inside a corporate structure where the same parent company owns the insurance plan, the PBM, the pharmacy, and often even the doctor groups and clinics writing the prescriptions. The company negotiating your drug price, the company paying the claim, and the company filling your prescription are the same company, wearing three different hats.

You probably figured out that CVS Caremark owns CVS pharmacies. But did you know it also owns Aetna, the third-largest insurer in the U.S.? Follow the chain from there. Express scripts owns Cigna. OptumRX owns UnitedHealth Group—by far the world’s largest insurer.

And this is somehow not a conflict of interest.

The FTC found that PMB-affiliated pharmacies captured 68% of specialty drug dispensing revenue in 2023, up from 54% just seven years earlier. These aren’t middlemen. It’s the whole supply chain, negotiating with itself. It’s like hiring a divorce lawyer and finding out they’re married to your spouse.

And then there’s the rebate system, the part where the train really starts running backward.

Drug manufacturers pay PBMs rebates—aka kickbacks—in exchange for getting a drug onto the covered list. These rebates are calculated as a percentage of a drug’s list price. Read that again. A percentage of the list price. In what other industry do you pay your negotiator more when they get you a worse deal? The higher the sticker price, the fatter the rebate, the more money the PBM makes.

USC Schaeffer Center researchers measured the relationship: for every $1 increase in rebates, list prices rose $1.17. The system doesn’t push prices down. It pushes rebates up. And you pay the inflated sticker price.

Look at insulin. Humalog cost $21 in 1999. By 2017 it hit $274, a 1,200% increase. When cheaper biosimilar insulins hit the market, the FTC alleged PBMs “systematically excluded them in favor of high list price, highly rebated insulin products.” Cheaper options existed. The PBMs chose the expensive ones. By 2019, one in four insulin patients couldn’t afford their medication.

Now, PBM defenders will tell you that most rebates pass through to plan sponsors, which helps reduce premiums. Fine. But your copay is calculated off the list price, not the net price. So the rebate might lower your premium by a few dollars a month. Your copay at the pharmacy stays high. One of those numbers you notice. One you don’t.

From Boring to Broken

PBMs started in the 1960s as claims processors: useful, boring, and by most accounts, effective. They saved employers 15% to 20% on drug costs. In the 1990s, drug manufacturers tried to buy PBMs outright. The FTC blocked it. But the unintended consequence was a consolidation wave inside the PBM industry itself. Fewer companies, bigger footprints.

By the mid-2000s, PBM revenue had shifted from flat administrative fees to a cut of each drug’s list price. The higher the price, the bigger the cut. That’s a machine that only runs in one direction once it starts moving.

No one woke up one morning and decided to make prescriptions unaffordable. They just woke up every morning and decided to make a little more money, and the math took care of the rest.

What Happens When You Stop Paying PBMs?

In 2018, Ohio’s state auditor discovered that PBMs had skimmed $224.8 million from Medicaid through spread pricing: charging the state one price, paying the pharmacy less, and pocketing the difference. $208 million of that was in a single year, on generic medications. Drugs that are supposed to be cheap. The average spread: $6 per prescription, or roughly three to six times what the service was worth. Meanwhile, 371 Ohio pharmacies had closed in the preceding five years.

Then Ohio banned spread pricing. The spread dropped from $6 per transaction to 20 cents. The state saved $184.4 million in fiscal year 2023. Pharmacy reimbursements went up. Cheaper for the state, better for pharmacies, same drugs. The only losers were the PBMs. Which tells you everything about what the spread was actually paying for. Ohio, to its credit, asked the question the rest of the country has been too polite to ask: what if we just… stopped paying PBMs to rip us off?

Ohio wasn’t an outlier. Kentucky turned up $123.5 million in annual spread. New York: $605 million over four years. West Virginia fired its PBMs entirely and saved $54.4 million in year one.

Then the FTC weighed in. Its January 2025 report revealed that the “big three” PBMs had generated $7.3 billion in excess revenue from specialty generic markups between 2017 and 2022. Tadalafil, a pulmonary hypertension drug: marked up 7,700%. Capecitabine, a cancer drug: 3,289%. This excess revenue was growing at a 42% compound annual rate, faster than most tech stocks. An FTC official noted the $7.3 billion “could be an underestimation.” The vote to release the report was 5-0. Unanimous, bipartisan, including the incoming Trump-appointed chair.

When states started passing transparency laws, the PBMs created group purchasing organizations in Switzerland and Ireland to handle rebate negotiations offshore. Lower taxes, harder to audit. Express Scripts’ version, “Ascent Health Services,” ran more than $750 billion in purchasing activity through Zurich.

When your response to transparency reform is to move your money to Switzerland, you are not an organization that was expecting to pass an audit.

The Human Cost, by the Numbers

One in five U.S. adults has skipped filling a prescription because of its cost. One in ten cuts pills or skips doses. A 2025 study found that 24% of insulin users were still rationing their medication in 2024, virtually unchanged from the 25% in 2017.

Seven years of reforms, headlines, congressional hearings, and insulin rationing barely moved.

The pharmacies are disappearing too. More than 7,000 have closed since 2019. Over 2,200 in 2024 alone. Eight per day. Eighty percent of rural independents are now reimbursed below the cost of buying and dispensing the drugs on the shelf. For every prescription filled, these pharmacies are losing money. And the PBM is the one setting the reimbursement rate. After Congress abandoned PBM reform in December 2024, 326 pharmacies closed in 10 weeks.

That feels like a pretty big competitive advantage for pharmacies like CVS, who control the PBM, the pharmacy, and a large insurer.

Nearly half of U.S. counties now have at least one pharmacy desert. In Philadelphia, a city of 1.6 million people, 250,000 residents have no pharmacy within a mile.

Everything Reformers Wanted (On Paper)

On February 3, President Trump signed the Consolidated Appropriations Act into law. Part of a larger spending bill, this part of the plan had broad bipartisan support and included the most ambitious federal PBM reform ever passed.

The law bars PBMs from pocketing any share of drug manufacturer rebates. Every dollar goes back to Medicare or the employer paying for the plan. Semiannual reporting on drug spending, rebates, spread pricing, and affiliated pharmacy steering becomes mandatory. On paper, it’s everything reformers wanted.

It takes effect in 2028. Some provisions in 2029. If your house were on fire, this would be the equivalent of Congress agreeing to call the fire department in the next budget cycle.

The day after the law was signed, the FTC announced a “landmark” settlement with Express Scripts requiring the company to delink compensation from list prices, move to cost-plus pharmacy reimbursement, and bring its Swiss accounting back on shore. projected savings: up to $7 billion over 10 years.

No fine. No admission of wrongdoing. Express Scripts had already announced a rebate-free model months before the settlement dropped. The settlement mostly puts a legal stamp on changes the company was already making. Less a punishment than a press release with a consent decree attached.

Full compliance: 2028. Monitoring period: 10 years. Approved by a commission reduced to one voting member.

The PBM trade group is already arguing the reforms will raise drug costs and premiums, the same prediction it has made about every proposed reform for the past two decades. Analysts expect PBMs will recapture lost revenue through administrative fees, private-label biosimilar medications, and new fee structures that technically comply with the letter of the law while preserving the same economics underneath.

The industry has two years before the rules kick in. It has never needed that long to come up with a scheme to keep up the status quo.

The $11 Alternative

So, here’s what happened.

A system built to negotiating drug prices got captured by three conglomerates that made more money when prices go up. Two decades of proof. Spread pricing. Rebate manipulation. 7,700% markups on cancer drugs. Shell companies in Zurich. Seven thousand dead pharmacies. And when the bill finally came due, the penalty was a settlement with no fine and a law that doesn’t start for two years.

The price on your prescription was never a price. It was a negotiating position between companies that are all on the same side.

And nothing there will change.

But alternatives exist, and real change is happening. Just not with the big three, or PBM models.

Mark Cuban’s Cost Plus Drugs sells that $6,229 multiple sclerosis medication for $11. Blue Shield of California walked away from the traditional PBM model last year and unbundled it into eight separate vendors. Your employer could demand pass-through pricing tomorrow, if anyone in HR knew to ask.

The question was never whether this system could work differently. It was always who benefits from making sure it doesn’t.

She knew something was wrong about four minutes in.

He was good-looking. He’d picked a good coffee shop. He asked her questions and waited for her to finish answering, which put him ahead of most of the dates she’d been on that year. But something was off. He talked about gender roles as if he was reading off a cue card. He used the phrase “high-value woman” without flinching. When she mentioned her job he said something about how it was great that she had ambitions but that she should make sure she still had the time to “be in her feminine.” It's not the kind of thing a person says. It's the kind of thing a person repeats. The difference is hard to articulate but impossible to miss across a table.

The date lasted 38 minutes. She said she had to meet a friend. He walked her to her car, which was polite. He said he’d love to see her again, which was fine. He touched the small of her back when he said it, in a way that felt rehearsed, like someone had told him not only to do it, but when.

She texted her friend from the parking lot. “Just had the weirdest date.” Her friend, after hearing the details, said she’d gone on the same date last week. Different guy. Same vocabulary. Same hand on the small of the back. Same slightly procedural energy that made the whole thing feel like a transaction neither woman had agreed to participate in.

Her friend’s version of the date featured a guy that keyed in on the same phrases, that held eye contact just a beat too long, that spoke and moved and mirrored her in a way that suggested he thought this was the way to build attraction, but hadn’t been told how attraction actually works.

You know this guy. He’s the human equivalent of a Subway franchise. He shows up on Reddit and TikTok and group chats where women swap stories about the man who claimed to be an “alpha male” unironically like it was a normal thing to say to a stranger. He gets memed. His messages get screenshotted. He gets discussed in that particular tone women use when something is both funny and unsettling at the same time.

Nobody asks where he came from, or why he exists. They should. Because that guy didn’t exist seven months ago. He was assembled. Pieced together bit-by-by as if run through an assembly line built for all the wrong reasons. It’s a commercial pipeline that took an average 24-year-old, sold him over $1,000 of apps, content, courses, communities, and supplements, and then delivered him to that coffee shop not as a better version of himself, but as a man women instinctively want to leave.

Every business in that chain got paid. None of them needed him to succeed. Most of them needed him not to.

The On-Ramp

He got dumped in March. He moped for a few weeks. He downloaded Tinder on a Friday night in April, which is when most men download Tinder. He didn’t know the app’s swiping mechanism was modeled on a 1948 B.F. Skinner experiment in which pigeons were conditioned into compulsive behavior through randomized rewards.

Tinder co-founder Jonathan Badeen admitted this on camera in an HBO documentary, comparing his users to the pigeons and describing the swipe as a slot machine pull. Nobody at the company stopped him from saying that out loud, on a film set, to a journalist. Which tells you roughly how little they worried about the comparison.

Swiping became a routine. He matched occasionally. He went on three dates in six weeks, all of which ended with a hug and a text that said “had a great time!” followed by silence. The app kept him swiping, because it was designed to keep him swiping.

A class action lawsuit filed against Match Group on Valentine’s Day 2024 alleged the company’s apps were engineered to be addictive, using the exact dopamine-manipulation techniques Badeen had described years earlier. Match Group, which owns Tinder, Hinge, Bumble, Plenty of Fish, Ok Cupid, and nearly three-dozen more dating apps, called the lawsuit “ridiculous.”

Tinder has 9.4 million paying subscribers. An estimated 75% of them are men. The app doesn’t need him to find love. It needs him to keep swiping long enough to hit the paywall.

If you want the full breakdown of how dating apps monetize the people they're supposed to be helping, I wrote about that here.

And he didn’t find love. He found the algorithm’s ceiling: a point where his free swipes ran out and the app suggested he pay $30 a month for more. He didn’t pay though. He deleted the app. He didn’t find love, and after using it for less than two months, he felt measurably worse than he did when he started. He was now a man who had been rejected by women and by the app designed to connect him with women, which is a specific kind of low that didn’t exist before 2012. And that feeling, the one where dating starts to seem like a system you’re losing at, is the exact emotional state that every business in the next stage of the pipeline is optimized to capture. A man who feels bad about dating will google “how to be more attractive to women” at 11 p.m. on a Wednesday.

That search is the top of an entirely different funnel.

The Content Ladder

YouTube’s recommendation engine did exactly what it was built to do. It took a sad guy searching for dating advice and, within 72 hours, graduated him through a content ladder: “how to be more confident” on Monday, “why women test you” by Wednesday, “the crisis of modern masculinity” by the weekend. The Movember Institute surveyed more than 3,000 males, aged 16 to 24, across the U.S., U.K., and Australia in 2025 and found that 63% of them routinely engaged with masculinity influencers. The ones who watched reported feeling more optimistic. They also reported worse mental health overall, more feelings of worthlessness, more negative attitudes toward women, and less willingness to talk to anyone about any of it. The content makes you feel like it’s working while the data says it isn’t. None of this is a side effect, it’s more of a retention model.

About a week in he found a creator with 2.3 million subscribers who made videos about self-improvement, discipline, fitness, and dating. The creator’s name was Hamza Ahmed. The videos were slick, featuring thumbnails with before-and-after physiques and titles like “Why She Lost Attraction” and “The Mindset That Gets You Girls.” The advice started out reasonably: go to the gym, read books, get off your phone. Then the vocabulary shifted. “Feminine energy,” “Frame.” “High-value male.” “Modern woman.” The words arrived so gradually that he didn’t notice when dating advice has taken a hard left into ideology, when self-improvement turned into self-optimization. He was at the point where improving himself looked like performing a version of masculinity that women recognize as a red flag five minutes into a coffee date. A peer-reviewed study in the Journal of Gender Studies confirmed the pattern: the content creates insecurity, then sells the solution to the insecurity it created.

He watched for two months. Then he saw the upsell. Ahmed runs a paid community called Library of Adonis, priced at $37 a month with roughly 2,500 members. That’s approximately $92,500 in monthly recurring revenue from one product. He also runs a second community at $47 a month and a free group with 88,000 members that functions as the top of the paid funnel. Ahmed once tweeted asking what bonus he could give members when they hit their second month to “decrease churn.” Churn. Not "guys who found girlfriends and moved on." Not "men who no longer needed help." Churn. The guru accidentally told you exactly what you are to him: a monthly recurring line item that might cancel.

Our guy joined. He paid $37 a month for four months.

He learned about “frame,” which is the idea that every interaction has a power dynamic and your job is to control it, to set the terms of the conversation so thoroughly that the other person is always responding to you and never the other way around.

He learned about “masculine polarity,” which means you act like a decisive, commanding leader and she responds to that energy the way a magnet responds to its opposite.

He learned how to structure a first date like a series of compliance tests.

He learned that if a woman pulls away, you should pull away harder.

He learned that “hypergamy” is the theory that women are biologically wired to trade up, always scanning for a man with more money, more status, a better jawline, and the only defense is to become so optimized you’re never the one who gets traded, to become a “Chad,” as it’s known in these groups.

He started dropping these words on dates. They didn't attract anyone. They did, however, prevent reproduction with perfect efficiency.

The community validated all of this. They told him he was improving. Nobody told him the coffee shop date would go the way it did, because nobody in the group had been on the other side of it. The push from the community was to continue optimizing, not pause and ask if any of them are optimizing the right things. And the group’s leaders had no incentive to ask these questions. Ahmed’s community needs him single the same way a gym needs you out of shape. Not maliciously. Structurally.

Upgradeable Components

The community pointed him toward products, the next level of his “journey.” Anabolics for increased gains at the gym. Jawline exerciser for that perfectly-chiseled face shape. Skincare routines marketed specifically as “masculine grooming protocols.” The same creators telling him to buy the products earned a cut when he did.

So he started buying. He bought the jaw trainer. He bought a greens powder. He bought a collagen supplement from a brand that sponsored Ahmed’s videos. He wasn’t being scammed, exactly. The products existed. Some of them probably did something to varying degrees of somethingness. But each purchase reinforced the same idea the pipeline had been building since April: that his body was a spec sheet with upgradeable components, and that if he optimized enough of them, the dates would go differently. The products didn’t fail him. They performed exactly as well as a $35 silicone chew toy can be expected to perform, which is to say it did nothing to increase his chances in that exact moment a woman decided whether she’d see him again. If that moment is about how you make her feel, he’d spent seven months and $1,100 learning how to make her feel like a variable in someone else’s equation.

Every dollar went to a different business. None of them coordinated. They didn’t need to. The pipeline works because each business is optimized to hand him off to the next one. The dating app creates the rejection. The content he’d found explains the rejection. The course sells the fix. The community reinforces the behavior. The products promise to optimize the lagging pieces in the spec sheet. And collectively, all of this keeps him single, which keeps him paying.

He walked into that coffee shop with $1,100 worth of installed software and no idea he was running a program riddled with bugs.

The Loneliness Gap

The whole machine runs on a single premise: that men are lonelier than ever and women are to blame. The loneliness data is more complicated than any business in the pipeline would like you to know.

Pew Research Center surveyed more than 6,200 adults in 2024 and found that 16% of men and 15% of women report feeling lonely all or most of the time. One percentage point. The male loneliness epidemic, as it’s commonly sold, is a gender gap so small it doesn’t clear statistical significance. Gallup found a wider gap among young people, with 25% of men ages 15 to 34 reporting loneliness compared to 18% of young women. That’s not nothing. But the gap is much smaller than anything a $6 billion industry built around it would suggest.

The pipeline doesn’t care either way. If the epidemic is real, the pipeline sells the cure. If it’s overstated, the pipeline sells the fear. The business model is indifferent to the actual suffering of its customers, which is the most efficient kind of business model there is.

And the off-ramp barely exists. Two volunteer-run subreddits, r/ExRedPill and r/IncelExit have roughly 31,000 combined members trying to help men walk back from exactly the kind of thinking the pipeline installs. The first academic study of these communities was published in 2023. Tim Squirrell at the Institute for Strategic Dialogue told VICE the work is “well tested” but nearly impossible to fund because it requires individual human connection. It can’t be scaled. It can’t be automated. Thirty-one thousand volunteers on a free platform competing against an industry that generates billions annually. The industry built the on-ramp with venture capital. The exit is staffed by people who are severely outgunned.

There is a woman named Izzy, 21, who told Refinery29 about her boyfriend Brian, who started watching Hamza Ahmed’s videos under the guise of self-improvement. He read books. He went to the gym. She thought it was healthy. Then she noticed the vocabulary. The same vocabulary our guy learned. The same words that showed up at the coffee shop. Izzy and Brian broke up a few months later. She told the magazine she couldn’t look at him the same anymore. She saw him for the lonely and insecure guy he really was.

Ahmed made $37 a month off Brian. Izzy lost a relationship because of what Brian learned for that $37. Ahmed has no idea who Izzy is, and his business model doesn’t require him to care.

And for those who don’t find the off-ramp, the pipeline has one more stage nobody in the chain wants to claim credit for. The vocabulary shifts one last time. “Frame” becomes “black pill,” the belief that attraction is determined entirely by genetics and that no amount of self-improvement will ever change the outcome. “Hypergamy” stops being a dating strategy and hardens into proof that the game was rigged from the start, that women are biologically incapable of wanting him, that the $1,100 he spent on optimization was a scam not because the products were fake but because no amount of jawline training fixes a system designed to reject guys like him. The self-improvement communities start to feel like lies. The incel forums don’t promise improvement, but what they do promise is an explanation that doesn’t blame him. It blames her.

The Parking Lot

He’s in his car. The date went well, he thinks. She laughed at his jokes. She’d stayed nearly the full hour they’d planned. He considers texting her tonight but remembers the 48-hour rule he’d read about in the community. He posts in the Discord group instead. “Had a solid date. She seemed into it but I couldn’t tell if she was testing me at the end. She said she had to meet a friend when I asked if she wanted to grab dinner. Thoughts?”

It took four minutes for a reply.

“Work on your frame, bro.”

He thinks he’s one tweak away. She thinks men are broken. Neither of them is entirely wrong.

Somewhere tonight a guy who just got dumped is about to open a search bar. In seven months he’ll be sitting across from someone at a coffee shop, repeating words he thinks makes him high-value, and she’ll know something is wrong about four minutes in.

“Entry-level” is one of those phrases that has undergone such complete semantic inversion that linguists should study it alongside “literally” (which now means figuratively) and “free shipping” (which you most definitely paid for).

Large-scale analyses of job postings found that over half of all jobs labeled “entry-level” require multiple years of experience. In IT, a similar large-scale analysis showed that 94% of entry-level jobs required at least a year of experience. That’s less of an entry-level job market and more of a bouncer at a club saying “Sorry, this club is for people who already got into the club.”

Conventional wisdom goes like this. Modern work is complex. Companies need qualified people. You have to prove yourself. This is just how competitive markets work, and if you can’t hack it, maybe you’re not cut out for the job.

And when a comprehensive analysis of workers revealed that over half of college graduates are still underemployed one year after graduation—working jobs that don’t align with their education—it’s easy to believe the problem is you.

Here’s what’s actually happening: Experience requirements aren’t about whether you can do the job. They’re solving an HR problem. And HR’s problem is that companies won’t fund adequate hiring capacity. The “3-5 years experience” requirement isn’t a skills assessment. It’s a filter. A crude, lazy filter that externalizes the cost of hiring onto you, while companies pocket the savings and complain about talent shortages.

So apply anyway. But we’ll get to that. First, understand you’re not deficient. The system is. And it’s broken in very specific, very profitable ways.

We Don’t Want to Train You

When you see a job posting requiring five years of experience for an entry-level role, you’re not looking at a mistake. You’re looking at a solution to a problem.

The average corporate job posting receives hundreds of applications. At big-name companies, it’s well into the thousands. Recruiters spend seven seconds per resume. At that rate, processing 500 applications is a full work week of doing nothing but scanning minimal text and making snap judgments—like corporate Tinder. Research shows virtually all Fortune 500 companies now use Applicant Tracking Systems to handle this volume, automatically screening most applicants as “unqualified.”

The experience requirement exists as what hiring managers—and I love this part—explicitly call a “first line of defense.” Not “first line of quality control.” Defense. Against whom? You. HR departments have adopted the language of military strategists to describe filtering out college graduates. This is the level of threat assessment usually reserved for foreign adversaries, not 22-year-olds with English degrees.

Industry surveys confirm that companies use experience requirements to “discourage applicants” and filter volume, not to assess job capability.

Here’s where it gets beautiful in its cynicism: Companies don’t even enforce their stated requirements. Career counselors recommend the “85% rule”—if you match 85% of the job description, apply anyway. Research found that 42% of jobs will hire applicants who don’t have the requested experience.

The requirements are theater. But the theater works. Applicants self-select out of jobs they’re qualified for. The ATS systems auto-reject based on keyword searches. And surveys reveal it’s breaking everyone. Nearly half of entry-level job seekers now lie on their resumes just to get past filters designed to eliminate them.

Everyone is lying to everyone, and everyone knows everyone is lying, but we all pretend the system works. It’s like cryptocurrency, but for employment.

The requirement isn’t about whether you can do the job. It’s about whether the company can defend the hiring decision if something goes wrong. More on that later. First, let’s talk about how we got here.

How Training Became Someone Else’s Problem

Your grandparents could walk into a bank with a high school diploma, get trained on the job, and become managers. Your parents got college degrees that led to corporate training programs that built careers. You? You need five years of experience for entry-level positions, receive no training, and face a decent chance of being underemployed for the next decade. The entry door didn’t disappear; it was locked from the inside.

A government report documented a 42% decline in employer-sponsored training between 1996-2008. That kind of collapse is normally reserved for industries disrupted by technology, like Blockbuster or taxis. Except in this case, nothing disrupted training. Companies just... stopped doing it. Recent data shows this hasn’t improved.

Why did this happen? Three reasons, each more depressing than the last.

First, declining employee tenure in the 1980s and 90s made companies ask “why train someone who’ll leave?”

Second, quarterly earnings pressure meant training budgets became easy cuts to juice short-term profits. (R&D went the same way, but that’s another story.)

Third, global competition pushed American companies into a race-to-the-bottom. Training workers costs money. As one analysis put it, this represents “a classic case of market failure. Firms invest less in training than is optimal from a societal and economic perspective.”

The cost of training didn’t disappear. It got externalized. Workers now bear it through unpaid internships. Universities bear it through “job-ready” curriculum pressure. Previous employers bear it by training workers who get poached. And when every company externalizes training simultaneously, nobody trains anyone. The system doesn’t produce new workers anymore. It just shuffles the existing ones around and blames graduates for not showing up pre-trained to jobs that don’t exist yet.

Capitalism’s Greatest Magic Trick

“Okay, so I’ll just get more credentials.” Great attitude. You’re going to be disappointed by how that works out.

The same research that identified the three-year requirement found something worse: Those requirements creep upward year after year. Three years may turn into four a decade from now. Five shortly after that.

This is happening while college degrees lose value. Analysis shows underemployed college graduates earn only 25% more than high school graduates, compared to the 88% premium that college-level jobs provide. Meanwhile, tuition has increased 141% over the past 20 years. Student debt exceeds $30,000. And research tracking millions of workers found that 45% of college graduates remain underemployed ten years after graduation.

You’re paying more, for credentials worth less, to qualify for jobs you still can’t get. The system has achieved the impossible: a perpetual motion machine where the entry requirements keep rising while the exit value keeps falling. It’s capitalism’s greatest magic trick—making something simultaneously more expensive and less valuable.

Experience Is a Luxury Good

And now for the part where we stop pretending this system is anything other than class warfare.

You can’t get experience without internships. Research shows that roughly half of all US internships are unpaid. Surveys found that 69% of college students can’t afford to take an unpaid internship. A separate University of Wisconsin survey showed most students who wanted internships couldn’t complete them due to financial constraints.

Education data revealed that only 33% of white students from low-income backgrounds take unpaid internships, compared to 60% of Black students and 53% of Latino students who feel compelled to take unpaid work. Data from the Class of 2023 showed paid interns got median salaries of $67,500, while unpaid interns got $45,000—the same as graduates who never interned at all. Working for free doesn’t just fail to help; it actively signals to employers that you’re desperate enough to be exploited.

And even after jumping through all these hoops, the same research tracking 60 million workers found that nearly half remain underemployed a full decade after graduation.

You did everything right. You got the degree. You gained the experience. And ten years later? There’s a nearly 50% chance you’re underemployed. The game is rigged.

The $26 Billion Rejection Industry

The system serves everyone except workers. Companies avoid training costs while maintaining legal liability protection. The HR technology industry sells Applicant Tracking Systems in a market expected to reach $26 billion by 2030. Universities sell degrees as “job tickets” while the actual job market degrades, then they pivot to selling “job-ready” certificates and bootcamps when degrees prove insufficient. Previous employers benefit by hiring workers trained elsewhere, creating a tragedy of the commons where no company invests in training because they assume everyone else will.

Workers lose. Especially young people, people of color, and anyone without family wealth. Here’s what nobody wants to say out loud: When every company externalizes training, nobody is responsible for creating new workers. The pipeline breaks. And then companies complain about talent shortages. With a straight face.

But there’s another dimension that makes this even more cynical. Employment law establishes that companies can face negligent hiring claims if they knew or should have known an employee was unfit for a position. Legal analysts warn that “the costs of defending negligent hiring claims alone can be significant and, coupled with an unfavorable outcome, crippling to a business.”

Hiring someone with “five years of experience” provides a paper trail showing the company exercised reasonable care. Experience requirements aren’t asking “Can you do the job?” They’re asking “Can we defend hiring you in court if something goes wrong?” The requirement protects the company from lawsuits, not customers from incompetent workers. Liability theater.

And then there’s wage suppression. Analysis found that jobs labeled “entry-level” pay 20% less than jobs requiring identical experience but without the “entry-level” designation. Hiring managers candidly admit they’re trying to recruit skilled professionals for the salary of a novice.

20% less. For the same work.

The Entry Door Is Closed

If you’re reading this and thinking “okay, but what do I actually do?” the answer is frustratingly conventional: Apply anyway. If you match 85% of a job description, apply. Companies frequently hire people who don’t meet their stated requirements.

But here’s the more important reframe: This is how entry-level became a luxury good. When you see a job posting requiring five years of experience for an entry-level position, you’re seeing the result of deliberate choices. Companies chose to stop training workers. They chose to underfund HR departments and use crude filtering instead. They chose to externalize costs onto workers, universities, and previous employers. They chose to treat developing new talent as someone else’s problem.

Some companies are experimenting with skills-based hiring. Some states have banned degree requirements for government jobs. It’s not enough. Not even close. Because the structural problem remains: a system that has decided creating new workers isn’t anyone’s job.

The next time you see an entry-level position requiring multiple years of experience, remember that this isn’t about you or your skills. This is about a society that has decided investing in people is someone else’s problem.

Welcome to the entry-level job market. The door is closed. The handle was removed in 1996. The sign still says “entry” because no one has taken it down yet.

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If you rent an apartment, you probably paid an extra $70 a month in rent last year. You didn’t know it. You couldn’t negotiate it. But you paid it. In some cities it was much worse. $181 a month in Atlanta. $136 in Denver. Nationwide, renters overpaid by $3.8 billion in 2023 alone.

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Your landlord didn’t act alone.

In November 2025, the Justice Department forced the company behind this system to shut it down. But for over a decade, the nation’s biggest landlords used an algorithm to do something that would’ve been illegal in a conference room.

This Wasn’t Normal

You know how all the apartments in your neighborhood cost about the same? Different buildings. Different management companies. But somehow the one-bedrooms all hover around $1,800, and the two-bedrooms cluster near $2,400.

That’s just the market, right? Wrong. In many cities, it was collusion.

In one Seattle neighborhood, 70% of the apartments—over 9,000 units—were controlled by just 10 property managers. Every single one used the same pricing software. Nationwide, the Biden-Harris Administration estimated that one in four multifamily apartments used this platform.

The company is called RealPage. And what they built was essentially a price-fixing cartel with really good UX.

Building a Cartel With Code

Landlords upload the kind of information renters normally don’t get to see. Not the rent they advertise, but the actual rent tenants pay—plus occupancy numbers, renewal rates, how many concessions they’re offering. Every secret they’ve got. In business school, they call this “competitive intelligence.” In law school, they call it “evidence.”

RealPage takes all this private data from landlords who compete with each other and pools it. Then, its algorithm looks at what everyone’s competitors are actually charging and spits out a recommendation for what each landlord should charge for an available unit. Updated daily.

And landlords follow these recommendations 90% of the time.

One landlord started using the software and raised rents within a week. Eleven months later? 25% more. RealPage literally marketed their ability to help landlords “exceed the market” by 2-5%.

But here’s the part that really gets me. One of RealPage’s own developers told ProPublica that leasing agents have “too much empathy” compared to what algorithms provide.

Think about that for a second. The problem, according to RealPage, is that humans feel bad about gouging renters. The solution? Remove humans from the decision. Which, if you think about it, is basically the mission statement of Silicon Valley: “What if we could be terrible to people, but faster?”

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The software also pushed landlords to stop offering concessions—no free months, no move-in discounts. It even suggested keeping units vacant rather than lowering prices. When everyone sees the same signals, nobody undercuts anyone.

Why “It’s Just an Algorithm” Isn’t a Defense

Federal regulators started using an analogy that makes this whole thing click. They call it the Bob test. No, I’m not making that up.

Imagine all the landlords in your city gave their private pricing data to some guy named Bob. Bob looks at everything—who’s charging what, who’s got vacancies, who just renewed their leases—and then tells each landlord what to charge based on what their competitors are doing.

That’s price-fixing. Illegal, right?

Now imagine Bob is an app.

“Your algorithm can’t do anything that would be illegal if done by a real person,” the FTC and DOJ wrote in a joint filing. The tool doesn’t change the rules.

For years, landlords claimed plausible deniability—they never talked directly. Sure, they used the same software, fed it competitor secrets, and followed its recommendations. But that wasn’t collusion, right?

Courts didn’t buy it. Federal prosecutors argued that “algorithms are the new frontier” of collusion. When competitors share nonpublic information through a common intermediary, who then recommends prices based on that pooled data, and the participants follow those recommendations? That’s coordination. You don’t need the smoke-filled room anymore.

What made RealPage illegal isn’t that it used fancy algorithms to analyze rental markets. Plenty of companies do that, and legally. It’s that it got competitors to feed the algorithm their secrets and then used those secrets to tell everyone what to charge.

That’s the line.

How It All Fell Apart

October 2022: ProPublica published a deep investigation into RealPage’s market penetration and how the whole system worked. Within weeks, the DOJ opened an investigation.

By August of 2024, the DOJ sued. Eight states joined. The complaint ran over 100 pages and included internal RealPage documents showing the company understood exactly what it was doing: helping landlords avoid “the race to the bottom” and ensuring that “a rising tide raises all ships.” It reads like a business strategy written by a fortune cookie.

In January 2025, the DOJ expanded the suit, going after six major landlords, including Greystar (the nation’s largest). Combined, these companies manage 1.3 million apartments in 43 states.

Greystar settled first, agreeing to pay $7 million to various states and to stop using competitor data for pricing. The others are still fighting.

Then in November 2025, RealPage settled.

Here’s what RealPage has to do: stop using real-time competitor data (only 12+ month-old historical data is allowed), stop local market targeting, kill anti-competitive features—like the “Governor” function that favored price increases—stop intelligence gathering, accept three years of court monitoring, and cooperate with prosecutors still going after landlords.

The whole thing takes effect in mid-2026. That’s 180 days to quit doing the thing they claim they weren’t doing wrong in the first place, but have agreed to stop doing. Got it.

But, and this is a big but, RealPage paid exactly zero dollars in fines and admitted no wrongdoing. Which is a hell of a deal, really. It’s settling a speeding ticket by promising to eventually slow down, maybe, if it’s convenient.

RealPage claims the settlement actually “blesses the legality” of its business model going forward.

Critics called it “lipstick on a pig.” The government says it restores competition. The truth is probably somewhere in the middle. But at minimum, the real-time data-sharing mechanism that enabled the coordination is now dead.

Meanwhile, 22 states have introduced legislation targeting algorithmic rent-setting. California and New York actually passed laws banning it. Cities from San Francisco to Minneapolis said landlords can’t use these systems at all.

The enforcement wave is just getting started.

What Happens Next?

Will your rent go down?

Probably not. The settlement kills the coordination. Landlords have to price independently again after losing access to a real-time view into what their competitors are doing. That should restore competition, at least somewhat. Maybe negotiation becomes possible again. Maybe concessions start coming back.

The underlying housing shortage is still here. Even the White House acknowledged that undersupply is the “root cause” of high housing costs. You’re still dealing with corporate landlord consolidation, rising property taxes and insurance, and general inflation. The algorithmic coordination was making a bad situation worse. But stopping it doesn’t make everything suddenly affordable.

What can you do?

Ask point-blank if the property uses algorithmic pricing software. Try negotiating. If agents or landlords won’t budge on anything, you’re probably dealing with the algorithm. Watch what happens through 2026, and report suspected violations to your state attorney general.

Find Your State Attorney General

The Bigger Picture

This is the first major algorithmic pricing enforcement victory. It won’t be the last.

Similar cases are already brewing in other industries, like hotels, gasoline, and e-commerce. The question is the same everywhere. Can companies use the same algorithms, feed them competitor data, and claim it’s not coordination?

The answer seems to be no. Analyzing data with an algorithm is fine. Using algorithms as a backdoor channel for price fixing? Not fine. The tool doesn’t change whether the behavior is legal. The Bob test works for any industry. If you couldn’t do it through a human consultant, you can’t do it through code.

RealPage marketed its software as making rental pricing “data-driven” and “scientific.” And it was, just driven by competitors’ secrets instead of actual market forces.

That’s not competing better. That’s not competing at all.

For a decade, landlords did something through an app that would’ve been illegal if they’d done it in a conference room. The algorithm made it feel legitimate. Automated. Neutral. Just “the market” working efficiently.

Except markets only work when competitors are allowed to compete. When they all use the same brain, fed with each other’s secrets, you don’t have a market.

You have a cartel with a pretty app.

You don’t have to go far to see one. Digital tip screens are everywhere: coffee shops, bakeries, even self-checkout lanes. Most people think tips are a little something extra for great service. Those people are wrong.

Tips don’t supplement wages. They replace them.

Here’s the scam: In 15 states, your tip counts toward the employer’s minimum wage obligation. The employer pays as little as $2.13 an hour, legally, and your tips are expected to cover the rest.

In the remaining states, employers must pay the full federal minimum wage. But they use tips to justify never paying more than the minimum. “Why pay more when workers get tips?”

Different system. Same result.

You pay their employees.

Every time you tap that screen, you’re not being generous. You’re subsidizing a business model. You’re generating data that helps normalize a system designed to transfer employer costs directly onto you while keeping wages frozen.

The tip screen isn’t asking for kindness. It’s asking you to do the employer’s job.

The Math That Makes It Work

The federal tipped minimum wage is $2.13 per hour. That wage hasn’t moved since 1991. 34 years. Before 1991, the tipped minimum rose automatically as a percentage of the regular minimum wage.

Then Congress froze it. Permanently, as it turns out.

Here’s how the math works in the states still using this system. An employer pays you $2.13 per hour. Your tips must bring you to the full federal minimum—which is shockingly still just $7.25 per hour. Work an eight-hour shift and your employer pays you $17.04 total. Tips must cover the remaining $41 to reach minimum wage. That $41 is called the “tip credit”—a term doing an impressive amount of heavy lifting to avoid saying “this part of the wage is on you, the customer.”

The tip credit represents 71% of the full federal minimum wage. Seventy-one percent!

Want some perspective? In 1991, gas cost $1.14 per gallon. Movie tickets ran $4.75. Stamps cost 29 cents. Today those same things cost $3.50, $15, and 73 cents. The minimum wage for tipped workers is still $2.13.

The only other thing that hasn’t changed since 1991? The McRib keeps coming back at McDonald’s. So, you know, consistency.

The Human Cost of Frozen Wages

Tipped workers have a 12.8% poverty rate, compared to 6.5% for non-tipped workers. Nearly double. Two-thirds are women. The median wage for restaurant workers, including tips, is $10 per hour. If that sounds low, keep in mind that half make even less than that.

Among waitstaff, specifically, 18.4% live below the poverty line. Only 14.4% receive employer health insurance, compared to 48.7% of workers in other industries. Inconsistent schedules. No benefits. Constant income instability.

But good servers make it up in tips, right? Yeah, some do. But they’re the exception. The median is closer to $10 per hour. The system doesn’t run on the handful that earn double that on a Friday night. It runs on the millions making poverty wages on a Tuesday lunch shift.

Wage Theft Is the Business Model

So if workers don’t make minimum wage after tips, the employer has to make up the difference. Problem solved?

Not quite. According to the Economic Policy Institute, Department of Labor investigations of 9,000 restaurants found 84% had violated wage and hour laws. Nearly 1,200 violations were specifically of the requirement to bring tipped workers up to minimum wage. Workers were cheated out of $5.5 million in those investigations alone.

When 84% of investigated restaurants break the law, that’s not an enforcement problem. It’s a design feature. Stealing from workers is illegal the same way going 56 in a 55 MPH zone on the highway is—technically prohibited, practically encouraged, and realistically leading to zero tickets for speeding.

We have more rigorous enforcement for selling lemonade without a permit than we do for systematic wage theft. An eight-year-old with a folding table gets shut down faster than a restaurant stealing from its workers 10 out of 10 times.

The Racist Origins of “Customary Tipping”

Tipping wasn’t always an American thing. It spread widely after the Civil War, when businesses were looking for ways to hire freed Black workers without paying them a comparable wage to their white counterparts.

The clearest example: American industrialist George Pullman hired formerly enslaved Black men as railroad porters. In 1915, Pullman paid them $27.50 per month for 400 hours of work. 7 cents per hour. (That’s roughly $835 today or $2.09 per hour.) Tipping wasn’t a bonus on top of their pay, it was built into the pay structure from the start, explicitly designed to save the company money.

Carry a bag. Get a coin. Hope at the end of the day for heavy pockets. (Let’s not forget our current minimum wage for tipped employees still sits at just $2.13 per hour.)

Americans hated this. Six states temporarily banned tipping in 1915, calling it “aristocratic condescension incompatible with democratic values.” By the 1920s it was entrenched anyway. All of the bans got repealed.

America in 1915: “Tipping is un-American.”

America in 1925: “Actually, tipping is extremely American.”

We contain multitudes. Mostly contradictions.

Tip Creep Is Not an Accident

72% of Americans say tipping is expected in more places than five years ago. That instinct is correct, for what it’s worth. Only 34% say it’s easy to know when to tip. Only 33% know how much to tip. 40% oppose businesses suggesting tip amounts on screens. 72% oppose automatic service charges.

And yet…

92% tip at sit-down restaurants. Only 25% tip at coffee shops. And while that doesn’t sound like much, it’s climbing fast. Tip screens are becoming ubiquitous and it seems there’s a lot of confusion about when, or if, we should be using them to hand out tips.

The suggested amounts have shifted too. Historical norms for how much we should be tipping have hovered around 10-15%. 20% if the service was excellent. Now point of sale systems routinely suggest starting at 30% or higher.

They’re anchoring you. It’s the same trick car salesman use when they show you the $80,000 car to make the $50,000 one seem more reasonable. Except the car salesman isn’t watching you reject him in real-time while you’re trying to buy a muffin.

Can you just press “no tip?” Sure. But the worker’s watching. So are the people behind you in line. The pressure isn’t accidental. It’s the point.

The Conformity Engine

Tipping percentages evolved slowly for most of American history. Roughly 10% was the norm in the early 1900s. 15% by mid-century. 20% by 2000. What took 50-plus years to move from 10% to 15% later happened in about five years, going from 15% to 21% between 2019 and 2024.

Why the sudden acceleration? A theoretical model published in Management Science explains the mechanism. “Appreciators” tip above average to signal genuine gratitude. “Conformists” tip to match social norms and to avoid awkwardness. Appreciators pull conformists upward, establishing a new baseline. Then the cycle repeats.

During COVID, this went into hyper drive. Average tips increased 2-5 percentage points and stayed elevated. People wanted to support workers during a crisis, which is a noble impulse. Generous tipping then became socially expected behavior. That temporary generosity became our new permanent floor.

In less than a decade, we reached a point where 15%—the standard for excellent service in your parents’ day—now marks you as cheap.

How $30 a Month Redefines Employment

Every time you tip at a coffee shop, you’re creating evidence that customers will pay workers directly at that type of establishment.

This data can justify reclassifying entire job categories as “tipped positions” eligible for the $2.13 minimum wage. POS systems track everything: what percentage of customers accept suggested tip amounts, how often they override the defaults, and whether they choose to opt-out entirely, avoiding tipping at all.

When coffee shop workers routinely receive tips, employers can argue these workers are “customarily tipped.” Federal law is explicit here: Workers who receive more than $30 per month in tips can be classified as tipped employees.

That’s it. $30 a month. If a barista gets a single $2 tip per shift for 15 shifts a month, they’ve crossed the threshold. The employer could legally cut their base wage from $7.25 to $2.13 and call it compliance.

The more industries normalize tipping through screens, the more jobs become eligible for sub-minimum wages.

The Trap Has No Exit

Individual choices don’t matter when the system controls the interface. More than 70% of people oppose various aspects of tipping culture, but still participate. And why wouldn’t they? There’s no individual solution to a systemic problem.

Tips transfer wage obligations from employers to customers while generating the compliance data needed to expand that transfer to more industries. You’re not tipping generously; you’re subsidizing a business model and building the dataset to extend it.

The workers are trapped. The customers are trapped. The only ones who aren’t trapped are the people with the greatest incentives to keep it this way.

Congress could fix the $2.13 wage tomorrow. They could adjust the $30 threshold to account for 60 years of inflation. They could close the loopholes that let 84% of restaurants steal with impunity. They won’t, because the system is working exactly as intended.

Every tip screen is a small transfer of obligation from employer to customer, and every year that transfer gets a little bigger, a little more normalized, a little more irreversible.

You’re not watching a cultural shift. You’re watching a business strategy eat the economy, one payment terminal at a time.

You know the routine. Open the app. Swipe. Match. Message. Maybe you meet. Maybe it goes nowhere. Maybe you delete the app for a week, then reinstall it at 11pm on a Tuesday—because what else are you supposed to do? Join a book club? Read the newspaper on a park bench and hope someone asks about your crossword? Actually strike up conversations with strangers? In this economy?

The apps promise connection. They deliver what feels like a second job.

Here’s the thing: Dating apps work. In 2019 39% of US couples met online. By 2020, 270 million adults worldwide had subscribed to a dating site. That’s 270 million people who decided to swipe right on disappointment. For context, it’s the entire population of Indonesia collectively deciding that meeting someone is too hard, let’s try algorithmic desperation.

But working and working for you aren’t the same thing. A review of 23 studies covering some 26 thousand users found that people on dating apps had worse psychological health than non-users. Worse. Not “about the same.” Measurably worse.

The apps aren’t failing. They’re just succeeding at goals that don’t align with yours.

How Success Became the Enemy

Match Group—which owns Tinder, Hinge, Match.com, OkCupid, and about forty others—generated $3.5 billion last year. Revenue actually grew 3%, which doesn’t sound like a lot until you consider that paying users dropped 5%. Revenue per user increased to 8%, about $19 per user.

Fewer customers, more money. Match Group isn’t expanding its base, it’s extracting more from the people already using its apps.

This creates the retention paradox. Netflix, for example, wants you to finish one show and start another. Dating apps? If they do their job well, you should leave forever. Delete the app. Stop paying. Get married and adopt a dog.

That’s “churn,” and every other business tries desperately to prevent it. But for dating apps, if their goals were aligned with ours, that’s exactly how they should work. Except that’s how you go bankrupt as a dating app.

Rotterdam researchers studied this exact problem. The apps faced tension between growing the network and growing revenue. The solution wasn’t optimizing for better matches; it was optimizing to maximize profitable users.

The apps could make more matches. The companies chose to make more money.

Want proof? In August 2025, Match Group settled with the FTC for $14 million. The charges? Sending users notifications about messages from accounts it had already flagged as fraudulent. Bots. Between June 2016 and May 2018, nearly half a million people subscribed within 24 hours of getting a notification that someone on the app was interested in matching with them.

The company knew the messages were fake. They sent them anyway.

The Gender Ratio Problem

Open Tinder. 75% of users are men. On Bumble, 67%. One analysis of 3,700 profiles found a consistent 2:1 ratio, at best, of men to women across platforms.

Women are roughly half the dating pool offline. Why are they a third of it online?

Simple answer: Platforms don’t recruit them. Women don’t have difficulty finding dates. They have difficulty finding qualified matches. And more dates doesn’t necessarily equal more qualified matches.

Real answer: The imbalance is more profitable.

Women have an average match rate of 30.7% on dating apps. Meaning, for every 3 people they like on an app, 1 will like them too. For men, it’s not quite as rosy. Men average 2.63% — a mere one reciprocated like for every 38 women they like on a dating app. Over the life of their app experience, men swipe right (like) an average of 16,368 times. Women? 2,283 times.

Different problems, same outcome: revenue. Men pay for boosts to solve scarcity. Women pay for filters to solve for overwhelming interest. Both are miserable, but both pay.

This is solvable. Match.com and eHarmony achieve near-50/50 ratios of men and women. Christian Mingle is 56% female. Even popular, yet imbalanced apps in the US are better elsewhere. Tinder’s ratio in Europe, for example, is near-50/50 — it’s 75/25 in the US (men/women). Same app. Vastly different ratios.

Apps that want balance achieve it. Apps that profit from imbalance maintain it.

The Algorithm Knows Who You’d Like. It’s Hiding Them.

Stanford researcher Daniela Saban partnered with a major platform to test its algorithm. Her modified version yielded 30% more matches. Nearly a third more matches from an algorithm tweak.

From this we can point to two truths. First, algorithms can be dramatically better than they currently are. Second, platforms choose not to improve them.

And if that doesn’t make you angry, this will. Saban’s team found that platforms track your matching success and use it to downgrade your shown profiles. Meaning, each match you get reduces your probability of seeing another quality prospect between 8% and 15%. When you’re succeeding, the algorithm’s job is to show you worse options. When you’re frustrated and thinking about giving up, it shows you the good ones.

The app knows which profiles you’d like. It knows. It has that information. It’s sitting right there in the database. But it withholds good matches until you’re unhappy enough to need them.

That’s not a bug. That’s not an oversight. That’s not “oops, our algorithm is still learning.” That’s a choice. That’s someone at Stanford proving it works 30% better, and someone in a boardroom saying: “Yeah, but what if we don’t do that? What if we make it deliberately worse? How much more money does that make us?”

And the answer, apparently, was “enough.”

Then there are the bots. The FTC lawsuit revealed that in some months between 2013 and 2016, more than half of instant messages users receive came from accounts Match had already identified as fraudulent. Match blocked these messages from paying members, then used them to advertise to its free users. Nearly 88% of the messages they sent during this campaign were later confirmed fraudulent.

The FTC found that 25% to 30% of daily Match.com registrations came from scammers. This wasn’t moderation failure. This was the business model.

Match settled for $14 million—0.4% of its annual revenue, which is essentially the FTC asking Match to Venmo them lunch money—noting that these practices “ended years ago.” You know, the way you “end” something after you’ve been caught, investigated for six years, and sued by the federal government. Except current estimates suggest up to 10% of profiles might still be fake.

A 2024 survey found 32% of users reported being catfished—talking to a user who was misrepresenting their identity.

Fake profiles inflate activity, keep free users engaged, generate notifications, and never lead to dates—but they succeed at keeping users on the app.

The matching tax. The bot economy. Both features, not bugs.

Variable Reinforcement, Dopamine Hits, and Monetized Desperation

These aren’t random choices. They’re components of architecture built to keep you coming back.

The apps use variable reinforcement, the slot machine mechanism. You don’t know which swipe yields a match, so you keep swiping. Insert coin, pull the lever, eventually the slot machine lights up and you win. Or so the thinking goes.

A pilot study found notifications predicted immediate mood improvements. Little dopamine hits. But increased time on apps predicted increased craving for more use—which is the literal progression of behavioral addiction.

Research published in JMIR Formative Research explicitly compared the business model to casinos: "Dating apps are like casinos in a way, in that they have to strategize where the reward needs to be—just enough to keep users coming back for more, but the reward cannot be so high that users walk away and not return. At least casinos have the decency to be honest about the odds. Slot machines don’t pretend they’re “designed to be deleted.”

A review of 29 papers found use patterns matching addiction criteria: salience, tolerance, conflict, relapse.

One study of 464 users aged 16-25 found frequent use led to excessive swiping, which led to upward social comparison, fear of being single, and partner choice overload.

The apps aren’t just failing to deliver relationships. They’re generating psychological distress in the process.

The Apps Hook the People Who Need Them Most

Arizona State researchers reviewed 23 studies, covering more than 26,000 users. Subjects had worse psychological health than non-users: more depression, more anxiety, and lower overall well-being.

Why do people keep using them? A survey of 521 users found the answer: hope. The desire for love and validation drives sustained engagement. The apps are most effective at hooking those who need them to work.

That’s the cruel efficiency of it all. You don’t open Instagram desperate for human intimacy. You don’t scroll TikTok hoping someone there might love you back. Dating apps are different because the stakes are different. People arrive vulnerable, looking for a connection that matters.

“Designed to be deleted,” Hinge promises. Meanwhile, the business model profits from keeping you in a state of perpetual, monetizable hope. “Designed to be deleted” the way a gym membership is “designed to get you fit”—technically possible, wildly improbable, and definitely not what keeps the lights on.

That’s not just extracting your attention. That’s extracting your vulnerability and selling it back to you as a monthly subscription.

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The Platforms Aren’t On Your Side. They Never Were.

Dating apps aren’t failing. They’re succeeding at misaligned goals.

The FTC proved platforms use fake engagement for profit. Stanford researchers proved platforms throttle good matches during successful periods on an app. Gender imbalances persist despite proven solutions. Users show worse outcomes across two dozen studies.

None of this is accidental. These are strategic choices embedded in the business model.

A subscription business profiting from continued engagement will optimize for continued engagement. When your success means their customer leaves, the rational strategy is making success just hard enough to keep you trying, but not so easy you succeed and stop paying.

They accomplish this through structural choices (gender imbalance creating scarcity for men and overwhelm for women), algorithmic choices (withholding good matches during successful periods), and direct manipulation (bot notifications that drive conversations). Each problem looks fixable. Together, they form a system optimized for profit extraction.

The platforms are not on your side. They never were. That doesn’t mean they can’t facilitate real connections; obviously they do. It means those connections happen despite how the system is optimized.

The apps work. They could work considerably better. they choose not to because working better is less profitable.

And now you know why your dating life feels like a second job. The difference is your second job doesn’t pretend to be “designed to fire you” once you succeed at it. Although, to be fair, your second job probably also isn’t actively hiding qualified candidates from you while sending fake emails from robots to keep you showing up.

So... dating apps are actually worse than a second job. Congratulations, Match Group. You’ve achieved something truly special: you’ve made working feel better than dating.

Jeff Bezos has taken out hundreds of millions of dollars in loans against his Amazon stock over the years. As of 2025, he still hasn’t paid back a dime of principal.

And he never will.

This isn’t a scandal. It’s not tax evasion. It’s how loans work when you’re worth $250 billion. Which is to say: they don’t work like loans at all.

Securities-backed lending now totals $175 billion. Billionaires live off these loans the way you live off your paycheck, except the loans never come due, never trigger taxes, and vanish when they die. (The billionaires die. The loans just evaporate, which is somehow more offensive.)

When you take out a $30,000 personal loan, you pay it back with after-tax income. You earn money, the IRS takes its cut, you use what’s left to repay the bank.

Bezos takes out a $500 million loan and… that’s it. That’s the whole transaction. The loan gets rolled over, refinanced, and eventually erased when he dies. Not because he’s richer, because the tax code treats his loan as a different species of financial instrument. Your loan has terms. His has suggestions.

The wealthy have figured out something the rest of us haven’t: Borrowing money isn’t the same as owing money. At least not if you borrow enough of it.

The Loan Isn’t Really a Loan

For normal borrowers, loans create obligations. For billionaires, loans create tax-free income.

Sell stock to buy a house? The IRS calls it a taxable event and takes its cut. Borrow $500 million against your stock? It’s a debt and you owe nothing.

Here’s how loans differ. Your auto loan has a 60-month term, fixed monthly payments, and an amortization schedule that chips away at principal every month. Miss a payment, your credit tanks. Miss three, they repo the car.

Billionaire loans don’t work like that. They’re built around flexibility, not terms. They’re interest-only, with no fixed maturity date. Pay us whenever. The bank doesn’t care because the collateral—real estate, businesses, or stock—keeps appreciating, making the loan safer every year.

When a loan matures, the billionaire doesn’t pay it off. They refinance into a new, larger loan, because the stock that was worth $500 million is now worth $700 million. The new loan pays off the old one, puts cash in their pocket, and resets the clock. Repeat until death.

This is the only context in which “rinse and repeat” ends with “and then die” and somehow that’s the best possible outcome.

The interest? Paid from stock dividends, taking out another loan, or just rolled into the balance. The principal? Never touched in their lifetime. Banks compete for these loans because they’re safe, and the terms just keep getting friendlier.

The strategy even has a name: Buy, Borrow, Die. The “die” part isn’t metaphorical. Death is when the entire scheme matures.

Most people try to avoid death. Billionaires found a way to make it tax-advantaged.

How “Buy, Borrow, Die” Actually Works

Three steps. Hiding in plain sight for over a century. All completely legal.

Step 1: Buy appreciating assets. This is the easy part if you founded Amazon or Tesla. Own things that grow in value (stocks, real estate, businesses) and never sell them. Selling triggers capital gains tax. Holding doesn’t.

This is why a lot of billionaires take $1 salaries. Wages trigger taxes. And wages are for suckers who need grocery money.

Step 2: Borrow against those assets. Banks and brokerages offer securities-backed lines of credit with terms you’ll never qualify for. They’ll lend 70% of your stock portfolio’s value, 90% if it’s in Treasury securities. Interest rates track the Secured Overnight Financing Rate (SOFR)—the base interest rate before banks add their cut—plus a small spread. This is far below the interest rate on your auto loan, your mortgage, or any debt you could qualify for.

Elon Musk pledged over $90 billion in Tesla shares as collateral for various loans. Larry Ellison has pledged 277 million Oracle shares, worth $82 billion, to secure personal loans. That’s more than the GDP of Slovenia, collateralized so an 80-year-old man can avoid paying taxes.

Step 3: Die. This is where it gets beautiful, from a tax-planning perspective.

When you die, your heirs inherit your assets at something called “stepped-up basis.” Under Internal Revenue Code Section 1014, the cost basis of inherited property resets to fair market value on the date of death.

Here’s how it works. You bought stock in 1990 for $100,000. By the time you die, in 2026, it’s worth $10 million. If you had sold it the day before you died, you’d owe capital gains tax on $9.9 million in appreciation.

But you didn’t sell. You died. And that changes everything.

Your heirs inherit the stock at a new cost basis: $10 million. Your original $100,000 purchase price? The IRS forgets it ever existed.

Now the estate sells stock to pay off your loans. But because the cost basis reset to $10 million, if your heir sells the stock to pay off your loan, the sale triggers zero capital gains tax. The $9.9 million you borrowed against for decades? Never taxed.

The IRS watches it walk out the door and shrugs.

When “Preventing Double Taxation” Prevents Single Taxation

Stepped-up basis had a purpose once. Congress wrote it into the Revenue Act of 1921, five years after creating the federal estate tax. The thinking was, if the government taxes an estate’s full value at death, it shouldn’t also tax the capital gains within the estate. That would be double taxation.

In 1921, this made sense. The estate tax kicked in at $50,000—roughly $900,000 today. Most substantial estates paid it. Stepped-up basis was the bargain: We’ll tax your estate, but we won’t also tax the gains made since you owned it.

That was then. Today, the federal estate tax exemption sits above $13 million per person. Only 0.1% of estates ever pay any estate tax. But 100% of heirs still get stepped-up basis. The provision that was supposed to prevent double taxation now prevents single taxation for 99.9% of the people using it.

The justification outlived its usefulness about 80 years ago, which makes it older than most of the senators voting to keep it… but just barely.

The Joint Committee on Taxation estimated that stepped-up basis cost the federal government $42 billion in 2021 alone. The Congressional Budget Office projects that replacing it would generate more than $110 billion over the next decade. That’s about what we spend annually on food stamps, except we’re giving it to dead billionaires’ heirs instead of hungry people.

Congress has tried to fix this. In 1976, it repealed stepped-up basis entirely. Four years later, it came back after businesses complained about record-keeping difficulties for old assets.

Obama proposed repealing it.

Biden proposed repealing it in three separate budgets.

Every proposal died without a congressional vote.

The 1921 loophole remains open in 2026 because the people who benefit from it are keeping it open. Which is impressive longevity for something that hurts 342 million Americans to protect roughly 700 families from paying their fair share of taxes.

What Billionaires Actually Pay

In 2021, ProPublica obtained a trove of IRS data covering thousands of America’s wealthiest individuals. What it found wasn’t surprising: The ultra-rich don’t just pay low taxes, they pay almost nothing. ProPublica calculated a “true tax rate” by comparing wealth growth to taxes paid.

Warren Buffett’s wealth grew by $24.3 billion between 2014 and 2018. He paid just $23.7 million in taxes. True tax rate: 0.1%. That’s not a typo. Ten cents for every hundred dollars of increased wealth. He paid less, as a percentage, than a Walmart cashier.

A teacher earning $60,000 pays roughly $8,500 in federal taxes, a 14% effective rate. Per dollar gained, that teacher paid 140 times Buffett’s rate.

And then there’s Jeff Bezos.

Bezos paid zero in federal income tax in 2007 and 2011. In 2011, when his wealth stood at $18 billion, he still claimed a $4,000 child tax credit. The credit is meant to help working families afford child care, or diapers. Bezos used it to reduce his tax bill from zero… to zero, but with a $4,000 refund.

None of this is illegal. It’s just the tax code doing exactly what it was built to do: Treat wealth and income as completely different things, then tax only one of them.

Too Rich to Audit

While billionaires pay nothing, the IRS audits people earning less than $25,000 annually at 5.5 times the rate of everyone else. Not because the poor cheat more. Because they’re cheaper to audit. These audits typically happen via mail, require less experienced agents, and lead to quicker recovery of funds—most often from miscalculations in the Earned Income Tax Credit (EITC).

Auditing a billionaire requires years of forensic accounting and lawyers that are often much more sophisticated than those employed by the IRS.

Natassia Smick is an interesting story that highlights the process. She earned $33,000 in 2017, and expected a tax refund of $7,300. Instead, the IRS froze it and spent 14 months auditing her over a $350 discrepancy.

During that same period, Bezos accessed billions in loan proceeds and paid zero in federal income tax. The IRS never questioned him. Not because borrowing billions while paying no taxes seemed less fishy. Because auditing Smick for 14 months costs less than auditing Bezos for a week.

70% Support. 0% Action.

70% of Americans support raising taxes on billionaires. Congress has done nothing. Not because reform is impossible. Because the people who’d pay those taxes fund the campaigns of the people voting on bills to raise or lower them.

President Biden proposed a 25% minimum tax on households worth over $100 million, affecting 10,700 people and raising $500 billion over 10 years. It was called “radical.”

One of Biden’s own fundraisers said it was a bill that was “dead on arrival and stupid to boot.” Senator Joe Manchin said, “You can’t tax something that’s not earned.”

Remember, unrealized gains aren’t “earnings,” even though you can borrow against them and spend that money like earnings. Apparently that’s a different thing.

Senator Ron Johnson of Wisconsin briefly threatened to vote no on the 2017 tax bill unless it included a larger deduction for pass-through businesses. He got his way. The expanded deduction netted two families $215 million in deductions in 2018. Those same families contributed $20 million to groups supporting Johnson’s reelection.

That’s a 2,500% return on investment for those keeping score at home.

This is how tax policy gets made. Billionaires fund campaigns. Senators insert provisions. Billions in tax savings flow to donors. The system sustains itself. Nobody is breaking the law. And it won’t change because those writing the laws are also the ones benefiting from it.

The Arguments Against Reform (And Why They Miss the Point)

The counterarguments against stepped-up basis, capital gains, or a wealth tax aren’t all in bad faith. Some are real problems. Understanding them explains some of the reasons reform keeps dying.

• Constitutional questions abound. The Supreme Court has never ruled on whether taxing unrealized gains is constitutional. Wealth taxes may require apportionment by state population, which would make them unworkable. California, for example, would pay more than Wyoming even if the latter had 10x the number of billionaires. Good luck passing that. But, who knows? Constitutional law is the only field where “nobody knows” is a valid expert opinion.

• Implementation is hard. No country taxes unrealized gains annually. Valuing public stock is easy, there’s a market price. Valuing privately held companies or an art collection? Harder. And what happens in down years? Does the Treasury cut refund checks to billionaires—actually, this might be the only thing that could kill the policy faster than lobbyists.

• They do pay a lot… in absolute terms. The top 1% paid over $1 trillion in 2021, nearly half of all federal income tax revenue. Measured by total dollars, the system looks progressive. Measured in percentages of total wealth, less so.

• Forcing asset sales could harm growth. If founders must sell stock annually to pay taxes on paper gains, they could lose a controlling interest in companies they built.

But here’s what those arguments miss. They’re defenses against a wealth tax. Eliminating stepped-up basis doesn’t require one. The IRS can allow this system to continue, collecting taxes upon death. Seems fair.

Remember how Buy, Borrow, Die ends: The billionaire dies, the estate sells assets to pay off the loans, heirs inherit what’s left. Under current law, stepped-up basis resets the cost basis to current value. Those sales trigger zero capital gains tax. The appreciation that funded decades of tax-free borrowing vanishes from tax rolls entirely.

End stepped-up basis and the math changes. Heirs inherit the original cost basis. When the estate sells assets to pay off loans, it pays capital gains on the full appreciation.

No constitutional issue, because you’re taxing realized gains.

No valuation problem, because there’s a documented sales price.

No forced sales beyond what already happens to settle the estate. And, if there’s enough liquidity in the estate, or cash to pay back the loans, no sale is required—the heir can simply refinance the asset with a new loan, after paying taxes and settling the existing debt.

The real reason stepped-up basis survives isn’t because it’s constitutional or practical. It’s that the 1921 bargain—we tax your estate, we don’t also tax the gains—quietly became a giveaway when the exemption rose from $50,000 to $13 million.

Two Tax Systems

Buy, Borrow, Die isn’t a tax loophole. It’s a parallel financial universe.

For most people, money is income. You earn it, it gets taxed, you spend what’s left. For the ultra-wealthy, money is collateral. It just has to sit there and exist.

Borrowing turns appreciation into spending power without ever having to sell.

Holding defers tax liability indefinitely.

Death wipes the balance sheet clean.

Stepped-up basis is where the trap door slams shut. Decades of untaxed gains—used as lifestyle fuel via loans—simply disappear when the debtor dies. Not deferred. Not discounted. Erased. What began as a protection against double taxation has become a guarantee of no taxation at all for the people wealthy enough to use it.

That’s why the debate gets so scrambled. Conservatives shout “wealth tax” when no one proposes one. “Small business” gets dragged into the debate from unrelated estate tax arguments. “Double taxation” echoes through chambers of congress even though, for nearly everyone using this strategy, there was never a first tax to double.

Ending stepped-up basis wouldn’t invent a new tax or punish business owners. It would just interrupt the final move in the Buy, Borrow, Die strategy. When assets are sold to settle loans, gains would be taxed the same way yours are.

No complex accounting. No new system. Just one rule in effect at the only moment money actually changes hands.

Your taxes feel oppressive because you live in a world where money has to move to be useful. Billionaires live in a world where money just has to exist. Loans become income, without ever being called income. Refinancing becomes routine. Death becomes tax deductible.

The glitch isn’t that billionaires found a loophole; it’s that we keep calling it a loophole after realizing it was how the whole system was designed.

In 2014, researchers at Yale and Cornell did something slightly unhinged: They measured the exact angle that cereal mascots look down from their boxes. Cap’n Crunch, Tony the Tiger, the Trix Rabbit, all positioned at a 9.6-degree downward angle.

Not 9 degrees. Not 10. Exactly 9.6.

Why? Because that’s the angle that creates direct eye contact with the average seven-year-old standing in the cereal aisle. When mascots make eye contact with children, the study found, brand trust increases 16%, and feelings of connection jump 28%. Children’s cereals sit 23 inches off the ground in the average supermarket—kid eye level. Adult cereals? 48 inches. Meanwhile, Grape Nuts sits at 73 inches because only people who have given up on life buy cereal that tastes like eating gravel with a spoon.

Scientists. With protractors. Measuring cartoon rabbits.

Let’s be clear about what’s happening. Food companies employ researchers to engineer trust in seven-year-olds who cannot possibly understand brand loyalty or manipulation. While parents try to make healthy decisions, brands are out here engineering emotional bonds with pre-adolescent children.

And it works. Your kindergartner has stronger brand loyalty to a cartoon rabbit than most people have to their spouses.

The 9.6 degrees isn’t happenstance. And it’s just the beginning.

Welcome to the grocery store, where nothing is an accident and you are the experiment.

You Came Here for Milk

You need milk. Just milk. Back-left corner. You know where it is, you’ve walked this path a thousand times.

By the time you reach the dairy case, your cart has Oreos you didn’t plan to buy, a rotisserie chicken that smells good, and a six-pack of La Croix. Your $5 milk trip just shot up to $28. And you’re still not to the checkout aisle.

This isn’t failed willpower. It’s science.

Essential items like milk, eggs, and bread get placed at the store perimeters on purpose. They force you to walk through the most square footage to get them. You’ve heard the adage a million times to avoid the interior, and shop the perimeter of a grocery store. Well, grocery stores know that too. That’s why they entice you with end caps, checkout lanes full of snacks, and a scenic path around the store that forces you to casually glance around while walking.

The industry calls this “building the basket,” which is a polite euphemism for “engineering mild financial amnesia aisle-by-aisle.”

Stores will tell you it’s about refrigeration logistics. Sure, that’s part of it. But retail research confirms that “department adjacencies are often purposely violated to encourage shoppers to travel through most of the store.”

The dairy pilgrimage you were just on. Not an accident.

And while you walk, they measure. Retailers use heat-mapping to track where you go while in the store. They use eye-tracking studies to nail down optimal shelf placement. Your loyalty card? It’s logging every purchase. You’re in a psychology experiment being graded in real time, and the test adjusts itself based on previous performance.

The results are quantifiable. Retailers make up to 10% more profit just from smarter layouts. Product exposure and sales increase with path length. The whole trip is designed to maximize spending, not convenience.

Then there’s eye level. Products at eye level sell far better than anything you have to reach for on the top or bottom shelf. Best-selling items get prime real estate. Store brands, even though they’re higher-margin, go down low where you have to crouch because they don’t move as many units as national brands. Because when’s the last time you bought generic Dr. Pepper and didn’t hate yourself after?

But it’s not just what you see. They’re coming for all five senses.

Bakery aromas get pumped toward the front of the store to make you hungry.

Stores run slightly cold because when you’re uncomfortable, you buy faster and get out. (More emotional decisions. More impulse purchases.)

Warm colors like red and yellow trigger appetite.

Green signifies healthy, whether it’s true or not.

Background music, lighting, the whole layout—all calibrated to trigger impulse buying. Our ancestors fought saber-toothed tigers and we’re at the supermarket getting seduced by climate control and fake croissants.

Does it work? Three out of four shoppers make unplanned purchases with each grocery trip. Impulse buying is emotionally-charged and spontaneous. Worse, it happens even when you know it’s happening. Behavioral psychologists who study this for a living still come home with cookies they didn’t plan to buy.

Invisible Price Increases

Let’s talk about “shrinkflation.” Here’s the mechanism: Shrinkflation means reducing the size of a product while keeping the price the same, or raising it. You’re paying more per ounce of product, even if the price stays the same. It’s inflation you can’t see coming.

It’s not new, but it went wild between 2021 and 2023 under perfect conditions: supply chain chaos driving up costs, inflation giving companies cover, and consumers too exhausted to track the changes. So companies shrank their packages. Then, even as supply chains stabilized and manufacturing costs dropped, the packages stayed small. The consumer price cut never came.

And there’s a reason it works. Consumers are more sensitive to changes in price than changes in quantity. Your brain instantly notices when $5.99 becomes $6.49, but tends to miss that 16-ounces became 14. Manufacturers know this. Recent research confirms consumers barely react to downsizing compared to an equivalent price increase.

Look at the damage. 60% of household paper products shrank during the pandemic. 44% of breakfast foods. 40% of snack foods. And while only about 5% of total goods shrank, those items that did represent a disproportionate amount of the ones you’re actually buying on a consistent basis.

Let’s name names.

• Gatorade went from 32-ounces to 28. 12% less sports drink, same price.

• Breyers ice cream shrank three times. From 64-ounces to 56, and then down to 48.

• Bounty paper towels went from 180 sheets per roll to 164.

• Doritos kept the same size bag, but 11.5-ounces dropped to 9.75.

• Chobani yogurt shed nearly an ounce, from 6-ounces per cup to 5.3.

And coffee. Coffee went from 16-ounce containers, to 13 ounces, and then down to 11 ounces. That’s 31% less coffee in the same-sized package. You’re getting a third less of the only substance that keeps you from driving your car into a lake most weekdays, and they didn’t even have the decency to make the container smaller so you’d notice.

I’ll do the math for you. If a package quietly shrinks by 25%, the old buying patterns have to adjust with it. Buy one container weekly? That’s 52 a year. You now need 16 containers annually to get what 12 months of the old size used to give you. At $8 per container, that’s $128 more per year. On just coffee.

Now do the math across cereal, chips, paper towels, yogurt, laundry detergent, and everything else that shrank in size while you weren’t looking.

This is the point where some argue that shrinkflation only contributed 0.01% to overall inflation between 2015 and 2021. So, is shrinkflation overblown?

No, not really. In the categories most affected, shrinkflation added 1.6 to 3 percentage points to inflation. Per-unit price increases ranged from 12% to 32% for paper towels and coffee respectively. The impact isn’t spread evenly. It’s concentrated in the products you buy most often, not the outliers that didn’t shift in size or price. Your personal grocery cart got hit much harder than national statistics suggest.

And that’s where it gets really unfair. If you can only afford the smallest package sizes, you pay more per ounce. A 16-ounce jar of peanut butter costs $4.99—that’s $0.31 per ounce. A 40-ounce jar costs $9.99—$0.25 per ounce. When the 16-ounce jar shrinks to 14-ounces, but the price stays at $4.99, you’re suddenly paying $0.36 per ounce. That’s a 16% price increase without actually raising prices.

Shrinkflation isn’t just sneaky. It’s regressive.

Oh, and the “family size” package? It’s often more expensive per ounce than the regular size. Retailers exploit a cognitive shortcut they previously trained us to believe: Bigger is always cheaper. And it usually is. Except when it’s labeled “family size,” “value size,” or similar—and then all bets are off.

Check the unit price, always.

How to Fight Back (Sort Of)

You can’t opt out of eating. But you can fight back. Kinda.

And look, I’m not the guy who writes the listicle about how to save money at the grocery store. But Consumer Reports did. Twice, in fact. Check them out here and here.

But what I can tell you is that even armed with this knowledge, you’re fighting a losing battle. The best you can hope for is that you have the discipline to stick to your list and not make a single detour. Or, try grocery delivery, if it’s available in your area (and affordable).

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The System Works Even When You Know How It Works

None of this is conspiracy theory territory. Store layout, artificial sensory enhancement, and shrinkflation are documented business strategies backed by decades of research. Entire consulting firms exist to optimize them. At a certain point, strategies designed to be subtle become impossible to ignore. And governments are beginning to take notice.

In 2024, France passed a law that requires stores to label downsized products. In the US, senators proposed a “Shrinkflation Prevention Act.” It died immediately, because of course it did. But the fact it was proposed at all says something about how visible it’s become.

Here’s what’s wild: The system is designed to work even on people who know exactly how it works. Behavioral psychologists who study impulse buying for a living? They still impulse buy. The people who install heat-mapping technology still get tracked by it. And that 9.6-degree gaze? It still works on your kid even when you’re standing there knowing precisely what it’s engineered to do.

So your suspicion was correct. You’re not getting worse at budgeting. You’re not imagining things. You’re not paranoid about the chip bag feeling lighter. The package did shrink. That cereal mascot is angled to look into the soul of your seven-year-old.

The exhausting part isn’t even that this is happening. It’s that it’s always been happening, we just didn’t have the words for it until recently. Every single grocery trip you’ve ever taken has been carefully choreographed. The milk placement. The music. The deliberate cold that makes you shop faster. Every bit of it, planned.

Grocery stores, consulting firms, and product manufacturers have been playing this game for decades. Go easy on yourself. You just learned the rules.