The Middle Class is a Semi-Meritocratic Pseudo Universal Basic Income

It will not survive contact with emerging AI technologies

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This is the part of your story where the AI tells you that the simulations it's running trend poorly for you.

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The numbers aren’t hypothetical anymore. They’re timestamps. Flags in the sand marking where the ground is beginning to give. You’re not watching a sci-fi scenario unfold. You’re living inside a schema collapse—a widespread quiet invalidation of roles, identities, and economic scaffolds once thought permanent.

At the root of it all is a paradigm shift most people haven’t named yet, though they feel it like hunger behind the eyes: the transition from Software-as-a-Service to Employee-as-a-Service. This isn’t just automation—it’s cognition, interface, and action collapsing into platform-native workflows. AI is not your assistant. It’s your replacement’s replacement. It doesn’t “take your job.” It makes your entire role indistinct from a feature set in a UI menu.

And when that happens, it happens everywhere.
Simultaneously.
Permanently.

The middle class was the cushion. The frictional labor force. The buffer zone where professional identities were sustained not solely for their output, but because the economic system needed somewhere to put the money. That function, too, is being made redundant.

The Function of the Middle Class as Economic Infrastructure

The middle class is not a moral designation or a cultural phase. It's a distribution mechanism. An engineered feedback loop in industrial and post-industrial economies to channel productivity back into demand.

You do not build a nation-state without it. You do not maintain one without it. And you certainly don’t survive global technological rupture without something performing that function.

But the middle class as it’s currently constructed is already slipping. In the United States, the share of adults in middle-income households dropped from 61% in 1971 to about 50% by 2021—and held there, brittle and precarious, through 2024. That trend holds across the Global North: polarization is replacing the center, and the remaining “middle” is often surviving through debt, dual-income exhaustion, and a steadily eroding sense of future.

We pretend these jobs exist for their outputs. Often they exist because the system needs people to be paid. This is why the middle class behaves like a pseudo-Universal Basic Income, delivered via white-collar occupation. Meritocratic mythology insists these roles are earned. But the truth is more infrastructural: middle-class employment is how wealth was laundered into stability. A kind of subsidized normalcy, as necessary to capitalism’s functioning as roads or bridges.

But if this is a subsidy, it’s a peculiar one—distributed not evenly or universally, but along a semi-meritocratic gradient. Promotion and compensation often correlate not with output, but with social proximity, credentialism, timing, or bureaucratic inertia. Middle management is full of people who get paid to do very little, and many of them know it. They count meetings, massage metrics, and oversee processes designed to sustain appearances, not outcomes.

And even when the labor is real, the structure signals something deeper: everyone seeks downtime within their compensated roles. This is not laziness. It’s adaptation. The desire for psychological breathing room is natural when performing tasks with ambiguous value or diminishing stakes. It reveals a core contradiction: we seek freedom within our labor, but our managers—enabled by surveillance tech and productivity dogma—are incentivized to eliminate every second of non-optimized time.

That tug-of-war is the tell. It reveals the truth about what’s being optimized: not productive output, but compliance, presence, and participation in a competitive scarcity ritual.

And that competition isn’t just external—it’s intra-class. The middle class is fragmented by design. Forced to scramble for advancement, for security, for “impact,” even when those terms are hollowed out. The need to signal merit—to prove one’s role is deserved—prevents many from admitting the system isn’t actually merit-based. It’s easier to play the game than to acknowledge the gameboard was always tilted.

This is why the pseudo-UBI framing lands. Because it explains both the arbitrariness and the indispensability. The jobs are real in their effect—they carry prestige, they pay mortgages, they stabilize demand—but their necessity is system-contingent. Not economic in the classical sense, but economic in the theatrical sense.

It was never built to withstand instant global labor invalidation.

And yet that’s exactly what AI just delivered.

From Software-as-a-Service to Employee-as-a-Service

The distinction between tool and actor is dissolving. Where we once licensed software to assist workers, we now license software to replace them. Employee-as-a-Service is not a buzzword—it’s the accurate descriptor for platforms embedding decision-making and production directly into the stack, disintermediating humans at the cognitive level.

In 2025, this shift is no longer speculative. It’s the baseline:

• GitHub Copilot and Replit Ghostwriter now generate up to 30% of new code, with 90% of engineers using AI in some form. At Google, 1 in 4 lines of new code is AI-generated.

• A single software engineer, empowered by generative tools, can produce at the throughput of an entire junior team.

• Which is exactly why entry-level coding jobs are evaporating. They are no longer economically necessary.

It doesn’t stop there. Marketing workflows have compressed: one human and a well-tuned LLM stack can generate blog posts, campaign assets, social content, and client-ready strategy decks in a fraction of the time. Advertising agencies are downsizing not because there’s no demand, but because one person can now do what used to require five.

In finance and HR, the gatekeeping layers are already digital:

• 99% of Fortune 500 companies use AI to screen resumes, and

• 40% now use AI for interviews.

Not augmentation. Replacement.

This is the industrialization of "thinking about things and writing them out for money."

It started with code.
It’s moving through copy, strategy, research, consulting, legal drafting, curriculum design.

And if your value proposition is your ability to organize thoughts into language that another human finds legible and useful—then statistically, you are already in the replacement queue.

Up next: What happens when roles become automatable everywhere they’re performed, at once. And what that means for regions, economies, and institutions built on the illusion of gradual change.

The Digital Collapse is Simultaneous and Non-Local

You don’t need to ship a robot to every town for digital collapse to arrive. You just need to update the model weights.

This is the defining difference between the blue-collar automation of the 20th century and what’s happening now. Factory jobs were lost in waves—each one geographically traceable. Detroit. Flint. Sheffield. You could stand on the street and see the machinery stop. But the collapse of digital work is placeless and synchronized. There’s no lag between an AI being deployed in Toronto and a job vanishing in Lagos.

Because the system doesn’t care where the human was. Only that the task can now be done without one.

Take Libra, the legal AI already assisting thousands of lawyers at over 150 firms. When it absorbs the function of junior legal research—contracts, memos, precedent summaries—it doesn’t matter if those juniors were in New York or Nairobi. The task is gone.

Or ChatGPT-powered tutors, now embedded in school districts across the U.S., helping students draft essays, prep for exams, and understand concepts in real-time. One AI instance scales across thousands of students. That means fewer hours needed from human TAs and tutors—whether they were gig workers in California or remote workers in Ghana.

In newsrooms, AI writes earnings reports, weather blurbs, and game summaries before human editors finish their coffee. These weren’t Pulitzer roles. They were the bottom rung. The way you got in the door. Now that door is sealed. Simultaneously.

This is task-based collapse. And tasks don’t respect borders.

Work in one place used to insulate another. We built careers assuming redundancy in location, time, regulation. Now, the disruption propagates globally, instantly.

A paralegal in London, a content writer in Mumbai, a recruiter in São Paulo—they're all operating on borrowed time if their task vector intersects with what an LLM can already do.

And the LLMs keep updating.
Faster than governments legislate. Faster than most people can re-skill.

You can’t outsource when the source has no location.

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Jobs vs. Work: Replacing vs. Reimagining

There’s a profound error at the core of most AI transition rhetoric, especially from the venture elite. It's the belief that "jobs" and "work" are interchangeable units. That if one disappears, another will slot in. That the market will reallocate purpose like a spreadsheet rebalances a portfolio.

This is spectacularly incompetent thinking.

OpenAI’s economic blueprint—nationalist in design, libertarian in instinct—imagines that productivity gains will somehow translate into social resilience. That’s fantasy. The kind you tell yourself when your incentive structure requires it.

Because jobs are not just mechanisms for producing value.
They are containers for identity, belonging, rhythm, and role.

They give people a place in the pattern. Remove them, and you don’t just lose income—you lose legibility.

Which is why the real danger is not the disappearance of work.
It’s the collapse of mapped value.

There’s plenty of work to do. Emotional labor. Caregiving. Local resilience building. Artistic contribution. But these are not jobs, not in the spreadsheet sense. They are often unpaid, untracked, unscalable. And the current economic operating system doesn’t reward them because they don’t neatly translate into capital accumulation.

So when AI wipes out the routinized middle—the admin, the research, the scheduling, the summarizing—it’s not “freeing people for higher-order tasks.”

It’s deleting the currency of participation.

VC Victor Lazarte doesn’t mince words: “It’s bulls--t… It’s fully replacing people.”

Not just in productivity.
In placement.

And if we don’t design new placements—not just retrain, but reimagine—we won’t have an economic middle. Just cognitive elites and the task-fragmented precariat.

The real question isn’t “what will people do?”
It’s who will get paid for doing it.

And the market, as currently constituted, doesn’t care.

Sectoral Unsustainability and Systemic Fragility

There is no physical front line.
Automation doesn’t need a factory.
It propagates across digital equivalence.

If a task becomes automatable in one company,
it becomes automatable in all companies doing that task.
The transformation is not linear—it’s ambient.

Consider the legal industry. Generative AI like Claude and Libra is now used daily by thousands of professionals. These systems aren’t replacing “lawyers” outright—they're replacing workflows. Contract review, research memos, discovery prep. The labor that used to justify a junior associate’s salary is now done in seconds. That’s why, by mid-2024, over 150 firms were using Libra, and the American Bar Association listed ChatGPT among its recommended legal tools.

Finance is undergoing a similar shift. Resume review and first-round interviews are now often handled by algorithms. According to one survey, 99% of Fortune 500 companies use AI for resume screening, and over 40% use AI bots for candidate interviews. This isn't just about efficiency—it's about redefining who gets seen at all.

Marketing, too, is experiencing compression. A single brand manager with access to an AI like Jasper, Midjourney, and Synthesia can ideate, write, illustrate, and publish a campaign—once the work of an entire team. Firms have scaled down accordingly. Entry-level jobs are vanishing.

Education? Teachers now lean on AI to handle lesson planning, grading, and individualized tutoring. What used to require a human assistant is now a plugin. Entire school districts have begun embedding generative models into their systems—not because it’s better, but because it's cheaper.

Meanwhile, in logistics, Amazon’s robotics division operates warehouse sections with no human oversight—"lights-out" environments governed by AI logistics engines. The same principle drives investment in autonomous trucking for long-haul deliveries. What factories did to blue-collar labor, warehouses and fleet systems are now doing to the last mile.

None of this is tied to geography.
It’s not a matter of where the job is.
It’s whether the job is abstractable into digital tasks.

If it is, it’s already on the glidepath to replacement.

The Real Threat: Productivity Without Prosperity

In theory, automation makes life easier.
In practice, it makes work scarcer and capital more concentrated.

The productivity gains from AI are real.
But they are not being redistributed.

Instead, firms are growing leaner, margins fatter, and payrolls thinner.
In Q4 of 2024, OpenAI reported over $3.7 billion in revenue—up nearly 1,700% from the year before. Its valuation jumped to as high as $90 billion. But this scale wasn't matched by hiring. It didn’t need to be.

Because machines don’t draw salaries.
Bots don’t pay taxes.

This is the core problem. Economic models are predicated on wage labor. Payroll taxes fund infrastructure. Income taxes support public goods. Consumption depends on people earning, then spending.

But if productivity decouples from employment—if value creation no longer requires human labor—then the foundations crack.

Former IBM CEO Arvind Krishna made this plain in 2023, stating that IBM would pause hiring for roles AI could replace. That translated to about 7,800 jobs. “I could easily see 30% of them getting replaced by AI and automation over a five-year period,” he said. By 2025, similar announcements are routine. The difference now is the sectors affected: legal, marketing, education, even healthcare admin.

This is not theoretical.
It’s observable in how output scales while payrolls contract.

The U.S. middle class already faced four decades of wage stagnation. According to the Economic Policy Institute, middle-wage workers saw just 6% real wage growth since 1979. High earners? 41%. AI doesn’t reverse that trend—it accelerates it.

It’s also why Nobel-winning economist Angus Deaton has advocated for direct cash transfers and stronger social safety nets: “There has to be a floor under the market, or the system will eat itself.” His sentiment echoes growing support for universal basic income, not as ideological idealism, but as structural necessity.

What happens when the machine gets more efficient,
but the people get less secure?

That’s not just a labor market challenge.
It’s a fiscal, social, and political one.
Because without wages, there's no tax base.
And without a tax base, the public infrastructure collapses under private extraction.

In the words of VC Victor Lazarte:

“It’s not augmenting, it’s replacing.”

And replacement without redistribution leads to failure—not of the machine, but of the civilization.

The Hollowing of Stability: Gigification and Debt

You don't need to be jobless to be destabilized.
You just need to be precarious.

In 2024, over 36% of American workers participated in gig work in some form—rideshare, delivery, contracting, freelance digital labor. That number is rising. But the old archetype of the gig worker—young, part-time, just making a little extra—is obsolete.

Many are now high-skilled, high-debt workers without employer benefits.
Software engineers on rolling contracts. Designers stitching together project work.
Adjunct professors with doctorates and no pensions.

The gig economy isn’t a supplement anymore—it’s a substitution.

The platforms have seen to that.
Uber, DoorDash, Fiverr, Upwork—none of them offer stability.
They offer an illusion of autonomy. But under the hood, it’s algorithmic labor extraction.

And the cost of living? Still indexed to full-time income.

Since 2020, U.S. home prices have increased by over 25%.
Medical costs continue to outpace inflation.
Student debt still hovers near $1.8 trillion.
Tuition at private universities now averages over $60,000/year.

So people take the jobs they can.
They rent the house they’ll never own.
They juggle three apps to make $100 by midnight.
They build a personal brand just to stay visible in search results.

This isn’t freelancing.
This is wage labor without the wage, employment without the employer, productivity without the protection.

This is working class with Wi-Fi.

And it has consequences. A 2023 Gallup poll found gig workers were twice as likely to report financial stress, and 63% more likely to struggle with healthcare access. Debt levels, mental health, life expectancy—they’re all correlated with economic instability. And that instability is being automated, scaled, and sold back to you as "flexibility."

Redistribution Is Not Enough: Sanctions, Tariffs, and the Accountability Inquisition

We cannot regulate collapse gently.
The scale of harm now requires punitive measures.

The destabilization of the middle class is not a byproduct—it’s a business model.
A model designed, approved, and funded by identifiable individuals.

The same executives, venture capitalists, and board members who attended Donald Trump’s second inauguration in January 2025—openly or discreetly—are the architects of this collapse.

They stood on the Capitol steps and smiled for the cameras as another tariff war was unleashed on the international community. Under the guise of nationalism, these policies gutted global cooperation and shielded U.S. tech monopolies from accountability. It wasn’t just political. It was economic warfare.

They’ve privatized the AI boom, externalized its costs, and are now consolidating power faster than regulators can blink.

So let’s name what’s needed:

A. International Sanctions on Collapse Drivers

• CEOs, who greenlighted mass layoffs and rebranded it as “efficiency.”

• Executives, who cashed their bonuses as their companies displaced entire industries.

• Boards, who failed to intervene, rubber-stamping extractive strategy after extractive strategy.

• Investors, who demanded unsustainable growth, enforced "AI-first" policy shifts, and backed companies like Mechanize—whose stated goal is to "replace every human worker."

This is not market failure.
This is orchestrated economic sabotage.

International bodies must respond with sanctions:

• Ban these individuals from sitting on additional boards.

• Freeze assets linked to companies causing demonstrable employment shocks.

• Restrict their access to international capital markets unless worker protections and redistribution mechanisms are in place.

B. Tariffs on AI-Extracted Value and U.S. Tech Dominance

Trump’s second administration escalated a unilateral trade war—weaponizing tariffs as bludgeons against allies and international institutions.

The logical counter-response is targeted tariffs:

• Impose import taxes on goods and services primarily generated by AI-driven U.S. tech companies with no meaningful labor contribution.

• Establish a tax floor for software exported from U.S. platforms where profits exceed defined thresholds but no corporate taxes are paid in receiving nations.

• Penalize companies that deploy AI systems across borders without contributing to local tax bases or reskilling infrastructure.

If the U.S. insists on a "sovereign-first" economic order, other sovereigns must respond in kind. Not through isolation—but through defensive coordination.

If OpenAI, Google, Amazon, and Meta continue to deploy generative AI tools that dismantle creative and cognitive labor markets globally—without consent, without compensation, without constraint—they must face consequences beyond fines.

C. Complementary Systems: Redistribution as Stabilization, Not Sedation

A punitive stance alone cannot repair what’s unraveling.

Even as we demand accountability from those who engineered collapse, redistributive systems must be scaled as stabilizers—not charity, not sedation, but systemic scaffolding.

That means wealth taxes keyed to AI-generated profits.
That means an international AI dividend: public revenue from private data and automated infrastructure.
That means worker ownership of the platforms that mediate their labor.
And yes, that means UBI, not as utopian fantasy but pragmatic necessity.

Policymakers resistant to sanctions may find common cause here. This is not about punishing success—it’s about insulating democracy from the volatility of market fundamentalism. The alternative is social entropy at scale.

We can’t regulate AI like it’s an app. We must re-regulate the economy itself.

D. Global Cooperation: From Tariff Retaliation to Shared AI Governance

Retaliatory tariffs must be the floor, not the ceiling.
But the ceiling? That’s cooperative infrastructure.

We need treaties, not tweets. Shared standards, not surveillance.
Global AI governance requires a new Bretton Woods—not just to manage technical risk, but to guarantee equitable economic distribution in a world where code writes code, and bots don’t pay taxes.

The AI Safety Summit was a start. The EU AI Act is a framework. But without enforceable commitments—on transparency, data rights, taxation, and labor obligations—these are paper shields in a digital war.

We must coordinate across borders to regulate across systems. Because collapse doesn’t respect jurisdictions. And capital isn’t loyal to countries—it’s loyal to its own expansion.

This is not radical.
It is responsive.

The alternative is capitulation to a new colonialism: not of land, but of language, logic, and labor.

The Choice Before Us: Systems or Collapse

This isn’t just about work.
It’s about worth.
And whether worth will be defined by stock tickers or shared dignity.

If we allow this trajectory to continue—automated value extraction without redistribution, platform power without democratic control, collapse without consequence—we will fracture.

But collapse is not fate.
It is engineered.
Which means it can be reversed.

We have the tools.
We have the receipts.
We have the names.

Now we need the will.

What happens next will not be determined by machines.
It will be determined by the systems we choose to build—or refuse to dismantle.