The Great Systems Collapse: What We’e Passing to Our Kids

We’re witnessing entire social systems—education, healthcare, taxes, and more—crumble as AI exposes how outdated their underlying assumptions have become.

Miriam is 26 years old, and every major system that’s supposed to support her path to adulthood has failed her in a different way.

She graduated from college in 2021 with a marketing degree and $87,000 in student debt. The education she received? Largely obsolete before she finished—professors teaching Facebook ads strategies from 2015 while TikTok was reshaping digital marketing. By senior year, she learned more from YouTube tutorials and AI tools than from $60,000-a-year classes. But she needed the degree because employers still required it, even though everyone knew it didn’t prove competence.

Now she works as a freelance “AI content strategist”—a job that didn’t exist when she started college. She uses ChatGPT and Midjourney to create campaigns for seven clients across four countries. She makes $73,000 annually, which sounds decent until you factor in no benefits, no retirement matching, and a tax situation so complex she pays $2,400 annually to an accountant who admits the IRS hasn’t figured out how to classify AI-generated income.

Last year, she spent three months trying to buy a small condo. She’d saved $40,000 for a down payment—five years of careful saving. She was repeatedly outbid by investment firms using AI algorithms to purchase properties 3% above asking price within minutes of listing. She gave up and continues renting a 450-square-foot apartment for $1,850 monthly—nearly half her take-home pay.

Her healthcare is a catastrophe. She pays $380 monthly for insurance covering almost nothing until she hits a $6,000 deductible. When chronic migraines started, she used an AI symptom checker that correctly diagnosed her in five minutes. Getting actual treatment required three months of waiting, $1,200 in copays for tests the AI had already identified as necessary, and a prescription costing $340 monthly because it wasn’t “covered.”

Last month, her younger brother was arrested for marijuana possession—a small amount that won’t be criminal in a few years after decriminalization, but was still illegal when he was caught. He’s in county jail awaiting trial, unable to afford bail, missing work, at risk of losing his apartment. The public defender met with him for seven minutes. An AI risk assessment flagged him “medium-high risk” based on zip code and traffic violations, making bail even less likely.

This is what system failure looks like from the inside. Not abstract policy debates, but daily life where every major institution that should enable stable adulthood is broken, inaccessible, or actively harmful.

Miriam isn’t unlucky. She’s normal. This is reality for tens of millions of young adults trying to build lives where every major system was designed for a world that no longer exists.

Why Systems Thinking Is Suddenly Everywhere

Systems thinking is a hot topic because people like Miriam are living through a collapse in real-time, and it’s becoming impossible to ignore.

We’re watching fundamental systems that have organized society for generations break down in obvious ways. Income tax is broken. College is broken. Prisons are broken. Healthcare, housing, employment—pick any major social infrastructure, and you’ll find systems designed for a previous era, straining under pressures they weren’t built to handle.

And AI is accelerating the collapse—not by attacking systems deliberately, but by revealing their fundamental assumptions to be obsolete. Every system rests on assumptions about human capabilities, information availability, time constraints, and coordination costs. AI is demolishing those assumptions faster than we can adapt.

The question isn’t whether these systems can be saved. It’s how much broken infrastructure we’ll pass to our kids, and whether we’re brave enough to rebuild from first principles while we still have time.

Miriam’s story reveals how a tax system built for 1920s workers is collapsing under the realities of AI-driven, borderless, multi-income digital life.

The Income Tax System: Built for W-2 Employees

Miriam’s tax situation illustrates how broken the system is for anyone whose work doesn’t fit 1920s categories.

The income tax system was designed around a specific economic reality: most people worked for a single employer, earned predictable salaries, and received W-2s at year’s end. That worked.

That world is vanishing. Miriam represents the future—gig economy, remote work, AI-generated income, global freelancing, multiple revenue streams that don’t fit any tax category. She has seven clients across four countries. She’s never physically met them. Her “work” involves using AI tools to create content. So whe

re does she owe taxes? Where was work performed—her Denver apartment, servers in Virginia where ChatGPT runs, or countries where clients are located?

When AI generates marketing copy that earns her money, who did the work? She prompted the AI, curated results, and delivered products. But GPT-4 wrote the words. Is that business income? Service income? Is she selling a product or a service?

Her accountant filed under five different income categories last year, making educated guesses about classifications the IRS hasn’t clearly defined. She paid $2,400 for this—money W-2 workers don’t spend—and still isn’t confident it’s correct.

Meanwhile, the wealthy deploy AI-powered tax optimization experts, exploiting system complexity for aggressive avoidance. The system punishes people like Miriam—straightforward income, modest earnings—while enabling sophisticated avoidance impossible without AI analysis of regulatory loopholes. Income tax assumes human labor, physical presence, and clear employer-employee relationships. AI obliterates all three. Rather than rebuilding for new realities, we’re forcing AI-age economics into 1920s categories. It won’t work.

The College System: $87,000 for Obsolete Education

Miriam borrowed $87,000 for a marketing degree. Her monthly loan payment is $780—more than many people’s rent. She’ll pay until she’s 36.

What did she get? Professors teaching outdated material. Social media marketing from 2014. Data analytics teaching Excel when industry used Python and R. Digital strategy never mentions AI tools already reshaping the field.

By senior year, Miriam learned more from free YouTube tutorials and AI experimentation than from paid classes. She used GPT-3 to help write papers, Grammarly to edit, Quillbot to paraphrase sections, avoiding plagiarism detection. She learned actual marketing from side projects and freelance work, not classroom instruction.

But she needed the degree. Employers still required it, even though everyone knew it didn’t prove competence. The degree wasn’t education—it was an expensive signal she could complete assignments and stick with something for four years.

Now she works in a job that didn’t exist when she started college, using tools that didn’t exist when she graduated, applying skills learned outside the classroom. And she’s paying $780 monthly for that increasingly meaningless credential.

AI makes this more absurd. Students use AI to write essays. Professors use AI to detect AI work. It’s an arms race where everyone knows credentials mean less yearly, but institutions can’t acknowledge this because their business model depends on pretending degrees still matter.

Alternative credentials are emerging—AI competency assessments, industry certifications, portfolio-based hiring. But Miriam’s generation is caught in transition: too late to benefit from the old system, too early to skip college entirely.

So they take on massive debt for partly obsolete education, increasingly disconnected from employer needs, then spend a decade paying it back while the system loads the next cohort with the same broken promises.

Miriam’s years of saving meant nothing in a housing market dominated by AI algorithms that outbid humans in seconds, turning homes into investment code instead of living spaces.

The Housing System: Algorithms Pricing Out Humans

Miriam saved $40,000—five years of discipline. Still couldn’t compete with AI-powered investment algorithms.

She spent three months seriously buying. Viewed dozens of condos, made offers on seven, was outbid every time.

The pattern was consistent: properties listed, and within hours—sometimes minutes—she competed against cash offers 3-7% above asking. Investment firms using AI algorithms identified undervalued properties and automatically submitted offers, beating individual buyers.

The algorithms had data she couldn’t see, analyzed comparable sales faster than humans, submitted offers instantly, and could afford overpaying because they optimized for portfolio returns across hundreds of properties, not finding one home to live in.

She never had a chance. Individual buyers with jobs and down payments can’t compete with institutional investors deploying AI-optimized purchasing strategies.

So she continues renting. $1,850 monthly for 450 square feet. Nearly half her take-home pay. No equity. No stability. No control over whether her landlord raises rent $200 next year (he probably will—his property management uses algorithmic rent optimization too).

Her parents bought their first home at 28 for $180,000 (about $280,000 today). They put down 10% on her dad’s single income. The house is now worth $650,000.

Miriam’s equivalent starter home costs $520,000. With 20% down, she’d need $104,000—more than twice what she already saved. And she’d compete against algorithms that don’t care about overpaying.

The system isn’t just hard. It’s broken. Housing has been financialized, and AI strategies are accelerating it. Homes are increasingly investment vehicles rather than places to live, and first-time buyers are systematically priced out.

The Healthcare System: AI Diagnosis, 1950s Delivery

Miriam’s healthcare shows the worst system failure: we have technology to do better, but institutional inertia prevents proper use.

When chronic migraines started, she used an AI symptom checker. Input symptoms—frequency, location, triggers, family history. The AI suggested three likely diagnoses, with migraine most probable. Recommended specific tests and treatments. Five minutes, free.

Then she entered actual healthcare.

First appointment: 11 weeks out. The doctor asked the same questions, ordered the same tests, and referred her to a neurologist. Another six-week wait. The neurologist confirmed the AI diagnosis from three months earlier and prescribed medication.

Total cost: $1,200 in copays before filling the prescription. The medication costs $340 monthly because insurance didn’t cover it—despite being a common, proven treatment.

Her insurance costs $380 monthly with a $6,000 deductible. She pays $4,560 annually in premiums before insurance covers anything meaningful. Then pays everything out of pocket until hitting $6,000.

She’s paying $380 monthly for “insurance” that didn’t help with $1,200 in diagnostic costs or $340 monthly medication. She might as well be uninsured until catastrophic events.

The absurdity: AI correctly diagnosed her immediately, free. The human healthcare system took three months and $1,200 to confirm what AI already knew.

We have technology for accurate diagnosis, treatment suggestions, and continuous monitoring. But we’re using systems designed around in-person visits, paper records, and insurance companies extracting maximum revenue while providing minimal coverage.

Miriam’s brother sits in jail because an AI risk score, built on biased data, labeled him ‘high risk’—a perfect example of technology making a broken justice system more efficiently unjust.

The Prison System: Her Brother’s Destruction

Miriam’s brother was arrested with a small amount of marijuana. In a few years, this won’t even be illegal—decriminalization is coming, just not fast enough.

He’s been in county jail for six weeks, awaiting trial because he can’t afford $5,000 bail. Lost his warehouse job after two weeks. About to lose his apartment. His public defender spent seven minutes with him and hasn’t returned calls.

An AI risk assessment scored him “medium-high risk” based on zip code, age, and two traffic violations. This influenced bail and will influence sentencing. The algorithm was trained on historical data reflecting decades of discriminatory policing, so it encodes and automates that discrimination while seeming objective and scientific.

Miriam watches helplessly. Her brother isn’t dangerous—he had personal-use marijuana. But the system will likely give him a criminal record, destroy employment prospects, make housing nearly impossible, and set him toward further criminal justice involvement.
This is supposed to be rehabilitation. It’s actually life destruction.

AI is making it worse—not through cruelty, but by automating bad decisions at scale. Risk assessments encoding historical bias. Surveillance flagging low-income neighborhoods for enhanced policing. Predictive systems create self-fulfilling prophecies.

We have technology enabling better alternatives: electronic monitoring instead of incarceration, AI-powered rehabilitation programs, personalized interventions. But we’re using AI to make a broken system more efficient at breaking people.

The Pattern: Automating Dysfunction

We’re not using AI to fix broken systems. We’re using AI to automate dysfunction at scale.

Income tax was already too complex and inequitable. AI makes it worse by creating income types not fitting existing categories while giving wealthy individuals AI-powered optimization that ordinary people can’t afford.

College was already unaffordable and disconnected from labor needs. AI makes it more irrelevant by doing work that students supposedly learn, while institutions pretend nothing has changed and charge $87,000 for increasingly obsolete credentials.

Housing was already difficult for first-time buyers. AI algorithms make it impossible by outbidding humans with superior information, instant decisions, and portfolio optimization.

Healthcare was already expensive and inefficient. AI can diagnose in minutes, but we still run three-month processes, charging thousands for confirmations of what algorithms already knew.

Prisons were already expensive and counterproductive. AI makes them more efficient at destroying lives through automated risk assessments encoding historical bias.

This is the infrastructure we’re passing Miriam’s generation: systems designed for worlds that no longer exist, failing at stated purposes, resistant to reform, and now being automated in their dysfunction.

Why Systems Break: Institutional Lag

Why can’t we just fix these systems?

The answer is institutional lag—the gap between when systems become obsolete and when institutions acknowledge and act on that obsolescence.

Institutions resist change because change threatens existing power structures, career paths, and revenue streams. Universities resist alternative credentials, threatening enrollment. Tax authorities resist reform, threatening bureaucratic jobs. Healthcare companies resist AI efficiency, threatening profit extraction. Housing policy protects homeowner wealth over affordability. Prison systems resist alternatives because incarceration has become an industry.

AI has accelerated change beyond what slow-adapting institutions can handle. The gap between “how things work” and “how things should work” widens exponentially.

Previous technological transitions gave institutions decades to adapt. AI compresses adaptation timelines to years or months. Systems designed for industrial-age employment don’t work for AI-age economics. We’re trying incremental adaptation when fundamental redesign is needed.

What We’re Passing to Miriam’s Generation

A tax system penalizing straightforward AI-augmented work while enabling sophisticated avoidance for the wealthy. Compliance costs consume thousands annually. A code so complex that even professionals guess at proper classifications.

An education system where degrees cost $87,000, teach partially obsolete skills, and create decade-long debt. Where credentials matter less yearly but remain mandatory gatekeepers. Where students learn more from free resources than expensive universities, but still must pay for the signal.

A housing system where algorithms outbid humans, institutional investors price out first-time buyers, and half your income goes to rent with no ownership path. Where home-ownership, defining middle-class stability for previous generations, is increasingly closed.

A healthcare system where AI diagnoses accurately, but three-month waits and thousands in costs are required for human confirmation. Where insurance costs $4,560 annually but doesn’t cover care until you’ve spent $6,000 out of pocket.

A prison system destroying lives over soon-to-be-legal conduct, using AI to automate historical biases, providing seven-minute legal consultations, and prioritizing punishment over rehabilitation.

The Window Is Closing

Miriam is 26. By 36, these systems will either be rebuilt or collapse entirely. We have maybe 5-10 years where intentional redesign is possible. After that, we’re in crisis management.

Her generation will inherit whatever we build or fail to build in that window.

Rebuilding means starting from first principles—redesigning taxes, education, housing, healthcare, and justice for an AI-driven world instead of endlessly patching obsolete systems.

What Rebuilding Requires

Start with first principles. What are we actually trying to accomplish? Given AI and modern technology, what’s the best way? The answer is almost never “patch the existing system.”

Accept that some systems need replacement, not reform. We’ve tried reforming for decades. It hasn’t worked. Income tax needs a complete replacement. College credentials need unbundling from education. Housing policy needs fundamental restructuring. Healthcare needs redesign around AI-enabled efficiency. Prisons need rethinking around rehabilitation.

Design for AI-age realities. Stop fitting AI-generated income into W-2 categories. Stop pretending four-year degrees are necessary when AI can provide personalized education. Stop allowing algorithms to price humans out of housing. Stop making people wait three months for diagnoses AI provides in minutes.

Move fast before the window closes. Every year we delay, more people take on debt for devalued degrees, pay thousands navigating incomprehensible taxes, get priced out of homeownership, and watch their families destroyed by counterproductive incarceration.

Be willing to threaten existing power structures. These systems don’t get fixed because fixing threatens those benefiting from current dysfunction. Reform requires confronting those interests.

Final Thoughts

Miriam is living through system collapse in real-time. Every major institution that should enable stable adulthood is broken, inaccessible, or actively harmful. She’s working hard, making responsible choices, and still falling behind because the infrastructure that previous generations took for granted has failed.

She’s normal. Tens of millions experience the same thing. This isn’t individual failure. It’s a system failure at scale.

We can do better. We have the technology. We have the knowledge. What we lack is political courage and institutional willingness to prioritize the next generation over preserving systems benefiting current stakeholders.

Miriam is 26. Her generation deserves better than inheriting our dysfunction. The question is whether we’ll give it to them.

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