The AI Layoff Tsunami Is Coming for Red America
Why Even Conservatives Must Start Thinking Like Socialists.
Over the past year, I’ve asked a lot of people what they think about AI, how it’s impacting jobs, and what it might mean for society. What I found was striking. Most people don’t yet understand the scale of the transformation that’s coming, or how quickly it will arrive.
Here’s why.
1. Most people don’t understand how AI actually works.
Even those who’ve tried ChatGPT or seen AI-generated images still think of these tools as clever tricks, not as the early signs of a much larger shift. Few understand how rapidly AI models are improving, or how close they are to handling complex reasoning, planning, and decision-making.
2. They’re not paying attention to the hardware side.
AI is tied directly to advancements in computer chips. As these chips get faster and more specialized, especially with AI accelerators and 3D-stacked designs, the systems running on them get exponentially more powerful. Most people haven’t made this connection.
3. No one’s really thought through what mass job loss looks like.
When entire industries shrink or disappear, what happens to the people left behind? What happens when millions of people are no longer needed for the labor that used to sustain them, their families, and their towns? Most voters, politicians, and business leaders aren’t ready for that question, let alone the answer.
This matters because the shift is already underway. It won’t take 20 years. It won’t arrive with a press release or a warning. It’s happening now, and most of the public will be caught off guard. They won’t have prepared. They won’t have voted for the kinds of policies that could have made the landing softer.
AI development is accelerating far more rapidly than most people realize. Having followed technological progress closely throughout my life, I’ve found that projections are usually too optimistic, largely because they fail to account for the countless unforeseen challenges that emerge during alpha and beta testing phases. But with AI, the speed and scale of progress are outpacing even the most cautious expectations most of the time.
“The first 90 percent of the code accounts for the first 90 percent of the development time.
The remaining 10 percent of the code accounts for the other 90 percent of the development time.”
—Tom Cargill, Bell Labs
That old joke about software timelines has held true for decades. Except now. With AI, nearly every recent prediction has been either right on time or too conservative. Some of the most important developments arrived years ahead of schedule.
Let’s look at a few examples:
Transformer models scaled exactly as predicted. This was the foundation for GPT-3, GPT-4, Claude, Gemini, and others.
Foundation models proved more powerful than specialized systems, just as some researchers forecasted.
LLMs were adopted far faster than expected. GPT-3 launched in 2020. GPT-4 followed in 2023. By 2024, entire sectors, writing, programming, support, were already being transformed.
AI coding assistants are outperforming many junior developers. GitHub Copilot, ChatGPT’s code interpreter, and Claude Opus are changing workflows faster than even optimistic engineers predicted.
To be fair, some predictions were premature. Self-driving cars and Artificial General Intelligence (AGI) still have a way to go. But they are getting closer.
Now we come to the political side of the problem.
If you’re conservative, and you believe in small government, self-reliance, and the dignity of work, the next decade will present a serious challenge to those values. Because tens of millions of working Americans, many of whom vote Republican, are going to find themselves out of work through no fault of their own. They will not be able to “work harder” to fix it. Many will not be able to “retrain” or become prompt engineers or AI whisperers. The labor market simply will not need them.
And when that happens, the question is no longer ideological. It becomes practical. How do we prevent a society from destabilizing when millions of people are cut off from economic participation?
This is not theoretical. It is already happening. The first wave of jobs is being automated in real time.
I almost never eat fast food, but on a recent visit, I was greeted at the drive-thru by an AI voice assistant, not a person. It handled my order perfectly, including changes and preferences. Customer service, both online and over the phone, is being rapidly replaced. Data entry jobs, claims adjusters, loan underwriters, and any task that is repetitive, rule-based, or binary is already being phased out.
This is just the start.
Let me break down the job displacement into three distinct phases so this will be a little easier to process, along with rough timelines for how and when this will unfold.
Phase 1: Immediate to Near-Term (2024–2027)
Target: Routine Cognitive & Basic Creative Work
Jobs where AI is already competent and economically advantageous.
Impacted Jobs:
Customer service agents (chatbots, help desk automation)
Paralegals & legal researchers
Junior content writers, copywriters, bloggers
Data entry clerks & form processors
Bookkeepers & low-level accountants
Translators (non-nuanced tasks)
Basic graphic designers (logos, simple posters, templates)
Technical support reps
Tutors (basic subject chatbots)
These roles depend on repetitive or templated language tasks, which large language models now outperform humans at in terms of cost, speed, and availability. Firms are already testing or deploying these solutions.
Phase 2: Mid-Term (2027–2032)
Target: Mid-Skill, Decision-Based & Some Physical Jobs
Jobs requiring more nuance, limited creativity, or partial physical interaction.
Impacted Jobs:
Radiologists and diagnosticians (AI-assisted imaging already rivals humans)
Traders & financial analysts (AI can digest and model faster)
Insurance underwriters & claims adjusters
Truck drivers (esp. long-haul, due to autonomous systems)
Warehouse workers (increasing robotic dexterity + logistics AI)
Proofreaders & editors
Middle managers focused on status updates/reporting
Junior software developers & QA testers
Real estate agents (partially automated sales + virtual tours)
This phase hits as AI becomes better at multi-modal reasoning (text + image + pattern detection), and robotics improves. Legal/regulatory and physical challenges delay full implementation—but many roles begin hybridizing (AI-human).
Phase 3: Long-Term (2032–2040)
Target: High-Skill Cognitive & Skilled Physical Labor
Jobs that require abstract reasoning, dexterity, social trust, or systemic synthesis.
Impacted Jobs:
Doctors (esp. general practitioners and diagnosticians)
Judges & legal analysts (with AI evidence parsing + precedent modeling)
Professors & lecturers (esp. in standardized education)
Software engineers (fully auto-coded systems)
Architects & engineers (AI-assisted CAD + generative design)
Construction workers (robotics + prefab tech)
Psychologists and therapists (AI companions increasingly used)
Financial planners and consultants
Executives (strategy assisted or replaced by AI pattern analysis)
This group resists automation the longest due to:
High levels of trust and judgment
Physical unpredictability in their environments
Complexity in system-level reasoning
But once AI systems develop robust agency, the ability to pursue goals across complex contexts, they begin encroaching here. And that’s where it really starts to get weird.
While estimates vary widely, credible projections suggest that the U.S. could see somewhere between 10 million and 45 million jobs displaced within the next 15 years, though many experts emphasize that both loss and creation will occur. However, on the whole, a lot of fields of expertise will be replaced by more exacting AI models that won’t make as many mistakes as humans.
92 million jobs will be displaced globally by 2030, but approximately 70 million new jobs are supposed to be created, suggesting a net global job shift. However, this isn’t accounting for population growth, so globally, there will be large decline in jobs, just within the next 5 years.
Why Conservatives May Need to Get a Little Socialist to Survive the AI Economy
For conservatives, the coming wave of AI-driven job displacement poses a deeper ideological crisis than most are ready to admit. It threatens not just workers, but the moral framework of the American right: the belief that work confers dignity, self-reliance sustains liberty, and markets reward effort. But what happens when the labor market simply doesn’t need the labor?
When AI systems can drive, code, file taxes, diagnose illness, write contracts, tutor students, and handle customer service, all at once, faster, and cheaper than humans, what exactly is the plan for the tens of millions of displaced workers, many of whom vote red? How does a society that ties basic survival to employment absorb 30, 40, or even 50 million people who are not lazy or unmotivated, but simply rendered economically irrelevant?
This is where conservatives face a historic crossroads. Either they cling to a fading vision of self-sufficiency and let economic obsolescence metastasize into populist rage, or they evolve, painfully, and pragmatically, toward a new social contract. One that admits: if markets can no longer pay everyone for their time, then society must pay people simply for being citizens. Not as charity, but as compensation for being shut out of the machine they helped build.
This is where Universal Basic Income (UBI) becomes not a progressive indulgence, but a conservative necessity. A monthly income of say, $1,000 or more, delivered unconditionally to all adults, would function like a national dividend. It would keep demand alive, prevent mass homelessness and despair, and preserve family stability in communities where jobs vanish but the mortgage remains.
There are other forms this support could take: conditional basic income tied to regions hit hardest by automation, negative income taxes that taper off with earnings, or guaranteed government jobs in infrastructure, caregiving, and climate adaptation. But all of them share a single premise: the old economy will not carry the population forward unaided, and pretending otherwise is not fiscal discipline, it’s political suicide in my hunble opinion.
The good news is that America can afford this. Between wealth taxes, carbon dividends, automation taxes, financial transaction fees, and reallocating parts of the defense or bureaucratic budgets, there are trillions in untapped resources. What’s missing isn’t money. It’s the ideological courage to admit that in an age of machines that think, freedom might mean getting paid for simply being human.
Where the Money Comes From: Realigning the Tax Code for a Post-Work Economy
If the philosophical hurdle is the hardest for conservatives to clear, the financial one is surprisingly manageable. Despite alarmist claims, Universal Basic Income is not some fiscal pipe dream. It’s about reallocating wealth in an economy already bursting with profits from automation, speculation, and consolidation, not creating money out of thin air.
1. Automation Tax (“Robot Dividend”)
As companies replace workers with machines and software, they enjoy large productivity gains while avoiding payroll taxes. An automation tax would:
Recapture a portion of those gains.
Function like a dividend for displaced workers.
Estimated Annual Revenue:
▶️ Tens of billions of dollars (depending on rate and scope of corporate automation)
2. Financial Transaction Tax (FTT)
A tiny fee on Wall Street trades—often less than 0.1%—could:
Generate massive revenue from high-frequency and speculative trading.
Add market stability by discouraging volatility-driven trades.
Estimated Annual Revenue:
▶️ $60–100 billion (with a 0.1% fee)
3. Wealth Tax
Popular among progressive economists and increasingly difficult to ignore:
A 1–2% tax on net assets above $50 million.
Targets ultra-wealth, not income.
Estimated Annual Revenue:
▶️ $250–300 billion
4. Carbon Dividends
Taxing carbon emissions would:
Incentivize cleaner energy.
Generate redistributable revenue for citizens and climate-affected regions.
Estimated Annual Revenue:
▶️ $100–200 billion (depending on carbon pricing and exemptions)
5. Closing Corporate Tax Loopholes
Current tax code inefficiencies and offshore havens allow:
Over $850 billion–$1 trillion in revenue leakage annually.
Even partial reform could reclaim a significant portion.
Estimated Annual Recovery:
▶️ Hundreds of billions (partial enforcement could easily fund UBI pilots)
6. Strategic Budget Reallocation (Defense)
Trimming even 10% from the Pentagon’s nearly $900 billion budget would:
Not harm national defense materially.
Free up enough to run a pilot UBI program for millions.
Estimated Annual Savings:
▶️ ~$90 billion
A New Social Contract or a Slow Unraveling
Conservatives have long championed stability, personal responsibility, and the sanctity of the American Dream. But dreams evolve, or die. In an era when effort no longer guarantees employment, and when markets can no longer price the value of millions of human lives, it becomes both morally and strategically essential to change the script.
UBI is not a utopian fantasy, nor a leftist overreach. It is the most market-friendly, fraud-resistant, and dignified way to maintain order, freedom, and prosperity in a future where jobs no longer define citizenship. Ironically, it may take a little socialism to preserve the very things conservatives hold most dear: family, faith, freedom, and national cohesion.
The alternative? A slow drift into resentment, conspiracy, and decay, where millions become disillusioned, democracy corrodes from within, and the populist right turns increasingly radicalized and reactive.
The American right has a choice. They can double down on nostalgia and rugged individualism in a world that no longer rewards it, or they can lead the next chapter: one that honors the worker, even when there’s no longer work to do. One that insists on human dignity, not because of economic productivity, but in spite of its obsolescence.
That may not sound like Reagan. But it just might save the republic.
Sources
Congressional Budget Office – Wealth Tax Revenue Estimates
https://www.cbo.gov/publication/55970University of Chicago Booth – Automation and Labor Market Disruption
https://www.chicagobooth.edu/review/how-automation-will-change-labor-marketBrookings Institution – Taxing Robots: What’s at Stake
https://www.brookings.edu/articles/taxing-robots-what-ai-means-for-the-future-of-work-and-taxationTax Policy Center – Financial Transaction Taxes: What, Why, and How Much?
https://www.taxpolicycenter.org/briefing-book/what-financial-transaction-taxEmmanuel Saez and Gabriel Zucman – How Wealth Tax Would Work
https://eml.berkeley.edu/~saez/saez-zucman-wealthtax2020.pdfInstitute on Taxation and Economic Policy (ITEP) – Corporate Tax Avoidance by Fortune 500 Companies
https://itep.org/fortune-500-companies-avoiding-taxes/Citizens for Tax Justice – Revenue Potential from Corporate Tax Reform
https://ctj.org/corporate-tax-sheltering-is-costing-the-u-s-hundreds-of-billions/Congressional Budget Office – FY2023 Defense Budget Analysis
https://www.cbo.gov/publication/58969Carbon Tax Center – Revenue Potential from Carbon Pricing
https://www.carbontax.org/policy/summary/Basic Income Earth Network (BIEN) – UBI Pilot Programs and Funding Mechanisms
https://basicincome.org/research/Roosevelt Institute – Modeling the Macroeconomic Effects of a Universal Basic Income
https://rooseveltinstitute.org/publications/modeling-the-macroeconomic-effects-of-a-universal-basic-income/Economic Policy Institute – The Case for Government Job Guarantee and Alternatives
https://www.epi.org/publication/the-case-for-a-job-guarantee/
I don't think the author of the article understands how AI works neither, in order get into phase 2 & 3 so early, the tech would have to be very lucky