The AI Doom Loop is a Myth

The AI Age Could Make White Collar Workers Rich Not Poor

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The Bull Case for AI: Why the Doom Loop Won’t Sustain

Lately, an extremely sharp, economically rigorous “Bear Case” for AI has been making the rounds courtesy of Citrini and chumba.

Citrini’s recent piece, in particular, made a splash today.

THE 2028 GLOBAL INTELLIGENCE CRISIS

The story goes like this: starting in the next few years, highly capable AI agents begin replacing white-collar workers en masse. Displaced professionals lose their six-figure salaries and slash their discretionary spending. This triggers a negative feedback loop, collapsing corporate revenues, causing more layoffs, cratering enterprise SaaS valuations, and starting a brutal default wave across the multi-trillion-dollar private credit and prime mortgage markets. Federal tax receipts evaporate just as the demand for government bailouts skyrockets. The doom loop feeds on itself until it takes us to a very dark place.

It’s a terrifying scenario. It’s logically coherent. And it’s built on a foundational error about how technological deflation propagates through an economy.

The bear case looks at AI and sees a machine designed to destroy the way we currently make money. The bull case looks at AI and sees a machine designed to destroy the cost of solving problems. These lead to radically different conclusions, but only if you take the timing problem seriously.

What This Means for Markets

The legacy enterprise SaaS market is in serious trouble. Markets have already worked this out. Per-seat pricing doesn’t survive a world where AI agents do the work of ten humans, and SaaS struggles in any scenario where the marginal cost of software drops. The multi-trillion-dollar private credit market, which financed leveraged buyouts of these software companies during the ZIRP era at 15-20x revenue multiples assuming perpetual per-seat growth, is going to take a haircut.

But this is localized creative destruction, not systemic collapse. The dot-com bust didn’t permanently destroy the economy because the underlying technology kept getting more useful even as overvalued companies imploded.

The broader equity market won’t collapse. It will rotate. We will get panic days along the way, clustered around weak job reports and earnings misses, but capital will move from companies that profited from cognitive scarcity (enterprise SaaS, professional services, staffing firms) to companies that profit from cognitive abundance (infrastructure, energy, compute, physical-world services). We’re already seeing the early stages of this.

Big Tech itself is more resilient than the bear case implies. A tech giant whose gross revenue drops 30% because consumers are broke will have also automated a massive share of its own costs. They will collect fewer dollars but keep a vastly larger percentage as profit. And these companies are the ones selling the AI agents to the rest of the economy. Falling labor costs lead to more AI investment, which accelerates the technology, which creates the new economic activity that displaced workers migrate toward.

The losers are identifiable: over-leveraged private credit, per-seat SaaS, the staffing economy, anyone levered to the displaced white-collar worker. The winners are equally clear: compute infrastructure, energy, physical-world buildout, and the platforms selling intelligence itself.

The Demand Explosion

White-collar layoffs are coming. As AI agents become highly capable, companies are going to use them to cut costs, and a significant number of people will lose their jobs.

AI is primarily an engine for supply-side deflation, the “good” kind. Prices don’t fall because we’re broke. They fall because the cost to produce things plummets. The bears will respond: sure, but AI attacks the labor market itself, which is the source of purchasing power. That’s a fair objection. But the scale of new demand will be much larger than the bears expect.

Bears expect a near-total wipeout of white-collar employment because they implicitly assume there is a finite amount of cognitive work to be done in the world. If an AI can do the work of 10 engineers, a company fires nine, keeps one, banks the savings, and the economy shrinks. Some companies will do exactly that. But on a macro level, this “fixed pie” assumption has been wrong after every major technological disruption in history, and for a specific reason.

Right now, almost every business on earth runs on triage. Because human cognitive labor is expensive, companies only build features, solve problems, and serve customers that clear a high ROI bar. Ask a tech company for a niche software integration and the product manager says no. They can’t justify $100,000 of engineering time on an “edge case” for 500 users.

When the marginal cost of cognitive labor drops by 90%, the triage era ends. A company might lay off 7 out of 10 engineers. But those remaining 3 won’t be doing the same work the 10 used to do. They’ll be directing fleets of AI agents to build things the company previously couldn’t afford to care about: software that adapts to every user, customer support that resolves problems instead of deflecting them, products localized for markets that were never worth the headcount.

Today, thirty million U.S. small businesses can’t afford outside counsel. Patients skip the specialist because the copay isn’t worth it. Students don’t get tutors because their parents can’t afford it. These people are consuming zero in sectors that bears are modeling as contracting. When those sectors get 90% cheaper, domestic demand expands into a population that was entirely priced out.

The bear case models a shrinking pie. The actual story is companies building what they never could have justified and millions of people buying what they were priced out of entirely. The pie is about to get much bigger.

Displacement Is Deflation

The sectors experiencing the most displacement are, by definition, the sectors where cost deflation arrives fastest. If a law firm uses AI to do the work of 60% of its associates, it has two choices: pocket the savings or lower prices. In a competitive market, the firm that doesn’t pass those savings to clients loses them to the one that does. Legal services don’t get cheaper five years after firms lay off associates. They get cheaper immediately, because the layoffs and the price drops have the same cause.

This doesn’t eliminate the pain, but it eases the transition.

What Actually Gets Cheaper

The bull case depends on cost-of-living deflation arriving fast enough to partially offset lost income. But cheaper legal research doesn’t help a displaced analyst make rent. The question is what happens to the line items that actually dominate a household budget.

Some categories deflate fast. Healthcare is the biggest. It’s the largest non-housing expense for most American families, it’s overwhelmingly driven by cognitive labor costs, and it’s already seeing early compression from AI tools. When an AI can triage patients, read imaging, and handle the billing bureaucracy that consumes a third of every healthcare dollar, costs fall structurally.

Education follows the same pattern. AI tutoring is approaching human-tutor quality at a fraction of the cost, and professional development gets radically cheaper precisely when displaced workers need it most. Financial services, tax preparation, and insurance are already commoditizing.

Food and energy deflate more modestly. Grocery margins are razor-thin, so even small efficiency gains from AI-optimized logistics and waste reduction pass through to consumers quickly. But food has a physical production floor. Energy benefits from AI-optimized grids and cheaper renewable design, but the buildout timeline is years, not quarters.

Housing is the main problem. Land doesn’t get cheaper because AI exists. Zoning doesn’t reform itself. Physical construction is peak Moravec’s Paradox, and the same labor demand that absorbs displaced white-collar workers keeps construction wages elevated. AI can compress design and permitting timelines, but the physical build is the dominant cost and it isn’t deflating during the displacement window.

The math: a displaced worker whose healthcare drops 40%, groceries drop 15%, and financial services drop 80% might see non-housing costs fall 25-30%. But housing is 35-40% of most household budgets and doesn’t move. Total expenses decline maybe 15-20%. That’s the difference between treading water and drowning, but it doesn’t close the gap for someone whose income fell 40%. The remaining delta is what makes the policy bridge non-optional.

Where the Surplus Goes

Cloud infrastructure is the direct precedent. AWS, Azure, and GCP operate as a profitable oligopoly, competing hard enough on pricing to prevent any one player from extracting monopoly rent, while each generating enough margin to fund massive capex buildout. Shopify, Stripe, Cloudflare, and the entire SaaS ecosystem were built on that oligopoly’s infrastructure, driving prices down for end users while the cloud providers kept printing margin.

The same structure is emerging in AI. A handful of frontier labs control the model layer. The businesses building on top of those models are already competing on price, and that competition is what pushes the surplus to consumers.

If that layer consolidates into a monopoly, the surplus gets captured as rent rather than passed through as lower prices. Displaced workers lose their jobs, but the cost of living doesn’t fall nearly fast enough to compensate. The model provider collects the delta as profit, the deflationary transmission breaks, and the bear case wins. A one-winner outcome at the model layer is the bear case for the broader economy, even if it’s a bull case for that company’s stock.

The Timing Problem

The entire bear thesis is fundamentally a sequencing argument: the pain of displacement arrives before the abundance of cheap intelligence does. Workers lose their jobs in Year 1, but the “good deflation” that makes a $70,000 salary feel like $120,000 doesn’t fully materialize until Year 4 or 5. In the gap, people default on their mortgages, private credit blows up, and the doom loop ignites.

This is the strongest version of the bear case.

First comes the compression. AI agents reach “good enough” capability for a wide range of white-collar tasks. Companies begin aggressive headcount reductions in the most automatable functions: back-office operations, basic legal and financial analysis, customer support, junior software development. Private credit portfolios loaded with leveraged software buyouts from the ZIRP era start taking losses. Unemployment in affected sectors rises. This is the period where the bear case looks most prescient, and it’s some of what we’re already seeing.

But overlapping with the compression, two countervailing forces build. The cost of services starts dropping as AI-powered healthcare, legal services, education, and financial planning reach consumers at a fraction of current costs. A displaced worker’s dollar stretches further each quarter.

Simultaneously, capital freed from cognitive labor costs rotates into physical infrastructure, energy, construction, and physical-world services that AI can optimize but robots can’t yet perform, creating demand for labor in sectors largely immune to AI displacement.

Eventually a rebalancing emerges. Nominal wages in many categories may be lower than their 2024 peaks, but real purchasing power has stabilized or improved due to relentless cost deflation. GDP growth, driven by the explosion of newly viable economic activity, generates the tax receipts needed to service debt.

The bull case doesn’t deny the pain of the compression. It argues that the countervailing forces begin early enough, and compound fast enough, to prevent the doom loop from becoming self-sustaining.

Deflationary doom loops in history (the 1930s, Japan’s Lost Decade) share a common feature: there was no countervailing force pushing costs down independently of collapsing demand. Things only got cheaper because nobody was buying. AI-driven supply-side deflation is categorically different. The cost reductions happen because the technology is improving, regardless of demand. An AI model that does legal research for $0.10 instead of $500/hour doesn’t stop getting cheaper because a recession hits.

Moravec’s Paradox as Economic Buffer

In robotics and AI, Moravec’s Paradox observes that tasks trivially easy for humans in the physical world (walking upstairs, snaking a clogged drain, pulling copper wire through a 1920s ceiling) are extraordinarily hard for machines. Meanwhile, tasks that feel sophisticated to us, like legal analysis, medical diagnosis, and code generation, are comparatively easy to automate.

Intelligence is easy. Handiness is hard.

This creates a multi-year gap that functions as a massive economic buffer. AI will drive down the design and engineering costs for next-generation nuclear reactors, high-speed rail, smart power grids, and millions of new homes. But because the robots aren’t ready to build them yet, we will need human hands. The capital saved on automated cognitive work rotates into paying humans to work in the physical world, where they’re still the only game in town.

This is already visible in wage data. Electricians, plumbers, HVAC technicians, and skilled tradespeople are commanding premium wages that have grown faster than inflation for years. AI-driven displacement of white-collar workers will push more people toward these fields, but the demand for physical infrastructure is growing even faster, especially with thousands of new data centers coming online.

The physical-world labor shortage runs deep beyond the skilled trades: delivery drivers, warehouse operations, home services, elder care. These roles don’t require years of retraining, which matters when the timeline for absorption is measured in quarters, not decades. For younger workers and those without deep professional identity anchors, the Moravec’s Paradox buffer is real and large. But it doesn’t catch everyone.

The Missed Landings

The displaced white-collar workforce has one structural advantage no previous disrupted labor class has had: the ability to hedge their own obsolescence. A $150,000 financial analyst with a 401(k), liquid savings, and basic financial literacy likely already owns the companies that are making him obsolete. Factory workers in the Rust Belt couldn’t buy equity in the robots replacing them, but a senior paralegal can buy shares in the platforms selling the AI that’s replacing her.

Still, the displaced workers who land on their feet share a common trait: optionality. They’re young enough to retrain, flexible enough to relocate, skilled enough to move up the leverage curve, or savvy enough to get ahead of this with their investing. The people who don’t land are the ones without those options.

They’re the 54-year-old contract attorney in a midsize city whose entire career is document review. The mid-level financial analyst who spent 20 years building expertise in a function an AI agent now handles in seconds. The back-office operations manager, the senior paralegal, the staff accountant at a regional firm. There are roughly 5 to 10 million workers in the U.S. whose skills map almost entirely to automatable cognitive tasks, who are deep enough into their careers that “learn to manage AI agents” isn’t a realistic path, and who live in markets without enough physical-economy demand to absorb them.

The cultural dimension shouldn’t be minimized. These are people who built identities around professional knowledge work. A senior financial analyst becoming a facilities manager for a data center campus may earn a comparable wage, but the psychic cost of that transition is significant. Multiply that across millions of households and you get a political force, not just an economic statistic.

But the core economic problem is simpler and more urgent: nominal obligations. A displaced analyst whose cost of living drops 20% over three years still has a mortgage payment due this month. Student loans, car notes, credit card balances: none of these adjust downward with deflation. For this cohort, the gap between losing income and benefiting from cheaper services isn’t an abstraction. It’s the window where they default, lose their home, or drain their retirement savings. This is where the doom loop has its best chance of igniting, not economy-wide, but concentrated in specific metros and demographics.

The good news is that this is a bounded problem. It’s not 50 million workers. It’s an identifiable cohort, concentrated in predictable sectors and geographies, facing a quantifiable income gap over a finite transition window. That makes it targetable, which is what separates a solvable policy challenge from a civilizational crisis.

The Policy Bridge

Even if living costs drop, a $3,500/month mortgage from 2024 still requires nominal dollars. If your income drops from $150,000 to $90,000, your mortgage-to-income ratio blows past sustainable levels regardless of how cheap groceries get. Debt doesn’t adapt to deflation.

Policy has to step in here, and this is where the bull case is most vulnerable to execution risk.

The Fed can buy agency MBS to keep mortgage spreads from spiraling. Congress can bridge the income shock with fiscal transfers. Because AI is simultaneously crushing production costs, the inflationary impact of that fiscal expansion is dampened relative to a normal stimulus cycle.

But if policymakers move too slowly, mortgage distress in tech-heavy metros could metastasize before cost deflation catches up. If they move too aggressively, they risk inflationary overshoot. The bull case requires that the right tools are deployed with reasonable competence, which, given Congress’s track record, is not a certainty.

COVID showed Washington can move fast when it has to — $2.2 trillion in emergency fiscal response in roughly two weeks. The AI displacement scenario also has a structural advantage COVID lacked: advance warning. AI displacement is arriving with years of lead time and an active policy debate already underway. And displaced white-collar workers in swing-state suburbs are exactly the demographic both parties compete hardest to protect.

None of this guarantees competent execution. Some amount of policy response will be needed to prevent millions of people entering poverty or losing their homes.

Long-term, the most powerful defense is the denominator. If AI unlocks massive productivity gains, a $30 trillion economy growing at 5-7%+ annually generates substantially more tax revenue even as individual wages decline. An AI-driven GDP expansion is realistically one of the few paths by which the U.S. might actually grow its way out of its $34 trillion national debt.

The Inequality Ratchet

Even in the bull case, where displaced workers find new roles and cost deflation stretches their dollars further, the distribution of gains is radically unequal.

When AI cuts a company’s cognitive labor costs by 60%, the surplus flows somewhere. Some goes to consumers through lower prices. Some goes to workers who remain, now leveraging AI to produce far more per hour. But the largest share accrues to the owners of the capital layer.

AI represents the final triumph of capital over labor. Every previous wave of automation shifted the balance incrementally. AI threatens to do it decisively, across nearly every cognitive function, within a single decade. For investors, this is the most important sentence in this entire piece. If you are on the right side of this trade, owning equity in the companies that provide, deploy, and build infrastructure for machine intelligence, the wealth creation will be historic. The companies selling the tools of cognitive automation will capture a share of every dollar of labor cost eliminated across the entire economy. That’s not a sector bet. It’s a claim on the broadest productivity transfer in modern history.

But the same dynamic that makes this so bullish for capital owners is what makes it politically unstable.

A displaced paralegal who retrains into facilities management might see her real purchasing power hold steady or fall modestly. Meanwhile, the equity partners at the firm that automated her role see per-partner profits double. You don’t need mass unemployment to generate political instability. You just need a large enough population watching a small enough elite capture a visibly disproportionate share of a growing pie.

A sovereign wealth fund is the most elegant solution. If the federal government takes an equity stake in the dominant AI platforms, whether through direct investment, licensing, or a negotiated exchange for the regulatory clarity these companies desperately want, it converts private rent extraction into a public asset. Norway’s Government Pension Fund is the proof of concept: a trillion-dollar sovereign fund built on resource extraction that distributes returns broadly across the population. If intelligence becomes the new oil, a public equity stake in the infrastructure layer is the cognitive equivalent of the Norwegian model.

The bears are right that unwinding the premise of scarce human thought will be chaotic. Private credit will bleed. Pockets of unemployment will be severe. The identity crisis will be real. But the bear case models a world where jobs disappear and nothing else changes. In reality, the same force destroying those jobs is simultaneously collapsing the cost of living, unlocking trillions in new economic activity, creating demand for human labor in the physical world, and expanding the fiscal space to manage the transition.

The biggest risk to owning the winners isn’t that the technology fails, it’s that it succeeds so well that the government can’t allow all of that value to concentrate in private hands.

And the white-collar workers at the center of this disruption are not helpless passengers. They are, uniquely in the history of technological displacement, a class of workers with the capital, the financial literacy, and the market access to position themselves on the winning side of the trade. The AI age doesn’t have to make them poor. For those who see clearly and act early, it can make them owners of the most transformative technological revolution in history.

On the other side of the transition, a worker earning $90,000 in a world where healthcare, education, legal services, and financial planning cost a fraction of what they do today may have more real purchasing power than he did earning $150,000 in 2024. Coupled with technological advances it’s quite likely he lives a happier and healthier life in this new world.

The transition will be harder than the optimists think and less catastrophic than the pessimists fear. The doom loop is a myth, but the promise of opportunity is not.

Good luck out there.

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Disclaimer: The information provided here is for general informational purposes only. It is not intended as financial advice. I am not a financial advisor, nor am I qualified to provide financial guidance. Please consult with a professional financial advisor before making any investment decisions. This content is shared from my personal perspective and experience only, and should not be considered professional financial investment advice. Make your own informed decisions and do not rely solely on the information presented here. The information is presented for educational reasons only. Investment positions listed in the newsletter may be exited or adjusted without notice.