Why Credit Creates Bubbles That Break the Economy

The asymmetric scaling of credit has inflated The Everything Bubble that will burst with devastating consequences for the real economy.

When credit scales faster than it can be absorbed by productive investments, the resulting credit-asset bubbles break the economy. This is the result of asymmetric scaling: credit (i.e. debt, money borrowed into existence) can be created in virtually limitless billions with a few keystrokes, while productive investments scale only incrementally.

The Federal Reserve added over $3 trillion to its balance sheet after the 2008-09 Global Financial Meltdown. That didn't automatically create $3+ trillion in productive uses for this tsunami of credit-money. Private banks also create money with keystrokes: when a lender originates a mortgage, that credit-money is created out of thin air. This is "the way the world works" because this new credit-money is based on the collateral of whatever property is being mortgaged.

This system has a pernicious circularity: as trillions of new credit slosh through the financial system, the wealthiest few with the highest net worth and credit ratings can borrow at lower rates of interest than the bottom 90%. They snap up houses for investment, outbidding those seeking a family home. Due to the vast scale of credit available, the higher bids push housing higher and higher, providing more collateral for more borrowing.

This is how credit-asset bubbles arise. Building a new enterprise is time-consuming and risky. It's much easier to buy an existing asset such as a house, commercial building, stock or corporate bond. As long as the asset appreciates at a rate higher than the interest being paid, it's wise to borrow more and buy more assets.

What happens when cheap credit chases existing assets is those assets appreciate due to the asymmetry of credit and the stock of existing assets: credit expands by the trillions of dollars, while the number of new assets being created lags far behind, as real-world buildings and enterprises can't be magic-wanded into existence with keystrokes.

This is how asymmetric scaling of credit and productive assets generates self-reinforcing bubbles: since credit is abundant, the assets being bid up appreciate in value, making it profitable to borrow even more and bid assets up even higher.

But since relatively little of this flood of credit is actually being invested in productive uses, the net result is a credit-asset bubble that reaches extremes and then collapses, destroying the phantom wealth created by excessive credit.

The fantasy here is that creating credit in vast quantities will automatically expand investing in productive assets. This is not what happens, because of the asymmetric scaling of credit, risk and return: it's far easier to borrow money and buy an existing asset that's appreciating / generating income than engage in building new housing or build a new enterprise that actually succeeds in generating sufficient revenues to make a profit.

Borrowing and buying assets is easy, building something productive is hard: that's asymmetric scaling in action. This is why private equity is snapping up veterinary clinics, specialty manufacturers and similar assets and then jacking prices to the moon once a quasi-monopoly has been established.

Once again we see the pernicious consequences of the asymmetric scaling of credit vs. real-world assets: private equity can borrow cheaply and at scale far beyond what households can borrow, and so they have the means to make owners of assets "an offer you can't refuse."

The owners of real-world enterprises are often struggling to pay bills, obtain insurance, retain employees, etc., and so when private equity comes with millions in untapped credit and makes an offer, few can afford to turn it down.

Private equity isn't interested in starting new enterprises, they're interested in establishing localized monopolies because these are so profitable and low-risk. Cheap (for the wealthy) abundant credit is what enables this pernicious cycle of more credit driving asset valuations out of reach of the bottom 90% and the assembly of quasi-monopolies that are rentier extraction machines that stripmine households to the benefit of those closest to the credit-spigot: corporations, private equity, billionaires, etc.

Burned by Billionaires: How Concentrated Wealth and Power Are Ruining Our Lives and Planet (new book by Chuck Collins)

Since it's tax preparation time, consider the tax break used by the wealthiest few to evade taxes. Rather than sell the assets they've accumulated with cheap credit, they borrow whatever sums are needed to pay their living expenses. Interest paid is a write-off, and since they don't pay themselves wages or sell any assets, there is no earned income or capital gains: no income, no income tax, and no Social Security-Medicare taxes, either.

The Federal Reserve created this asymmetric scaling credit monster to goose the wealth effect: the richer we feel, the more we borrow and spend. But that's not all that happens: the wealthiest few borrow more to buy up existing assets, pushing them out of reach of the bottom 90% and enabling monopolies that extract wealth not by creating better products at lower prices but by jacking up prices for products and services of lower value.

Here is a chart of the S&P 500 stock market index (SPX). Absent the injection of trillions in credit and the resulting credit-asset bubble, stocks would be expected to track the economy, i.e. GDP. If stocks had tracked GDP growth, the SPX would be roughly half its current lofty level: 3.450 rather than 6,800.



If housing had tracked inflation, it would be at valuations 40% lower than current valuations.



The Federal Reserve reversed the decline of valuations in Housing Bubble #1 by socializing the mortgage market, buying up $1+ trillion in mortgage backed securities (MBS). The Fed now owns over $2 trillion in MBS, so when Housing Bubble #2 (2020-2026) bursts, they won't be able to ride to the rescue. The asymmetries of scale will succumb to gravity.



A funny thing happens on the way to the wealth effect: the already-rich get much richer, and everyone else is left behind in The Stockyard of Unaffordability. here is a chart of housing unaffordability.



The asymmetric scaling of credit has inflated The Everything Bubble that will burst with devastating consequences for the real economy. What scales even faster than credit is risk-off fear.

Where does all this leave the rest of us? Two things to consider:

It's harder for bad things to happen when you have no debt.

Greed is a wonderful motivator but fear works much faster.




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Why AI Malware (and Harmful Second Order Effects) Are Out of Control

Fixing all this doesn't scale. What scales is the spread of uncontrollably harmful consequences.

When something scales faster than it can be absorbed or controlled, the resulting extremes break the system. That's the problem of asymmetric scaling. Let's take a current example: the malicious use of AI and the runaway expansion of harmful second-order effects generated by the explosive adoption of AI tools and agents. (Second-order effects: consequences generate their own consequences.)

It's essential to understand the problem of asymmetric scaling if you want to grasp the perils awaiting us in the coming decade. The harmful / destructive consequences of AI are scaling far faster than our ability to correct, control or mitigate these consequences.

Malicious use of AI is scaling far faster than countermeasures. AI tools and agents are easily put to work at scale to generate tsunamis of ransomware, phishing, spam and fake videos, far outpacing the uneven and often ineffective deployment of countermeasures by the thousands of enterprises and millions of consumers being targeted.

In terms of maximizing profits (i.e. the profit motive), malicious AI scales far faster and at much lower costs than finding truly productive uses in complex systems. Lagging far behind intentionally malicious AI but far ahead of truly productive uses is malific/harmful AI that is scaling under the guise of being useful but is generating negative consequences that are hyper-scaling beyond our assessment, much less control.

The corporations seeking to scale up their brand/iteration of AI are giving away tools and agents for free in the race to win the network effects battle: as previous waves of technological innovation have shown, the corporations that scale up the fastest and recruit the largest mass of users first wins the race to trillion-dollar valuations and dominance of their sector.

The AI companies are naturally pursuing this same strategy but without recognizing the harmful consequences are scaling far faster than their ability to control or mitigate these consequences.

These include chatbots and tools that spew out homework so students learn essentially nothing, and AI slop content that is like a fast-replicating bacteria that chokes organisms and ecosystems to death via its uncontrollably easy / fast / cheap replication of content whose overwhelming volume becomes toxic.

The many other harmful / destructive / malefic consequences and second-order effects of scaling AI adoption include:

1. Hallucinations presented as facts.

2. AI psychosis.
New study raises concerns about AI chatbots fueling delusional thinking First major study on 'AI psychosis' suggests chatbots can encourage delusions among vulnerable people.

2. Reasoning Theater (presenting a false screen of "thinking" to hide their shortcuts)
Reasoning Theater: Disentangling Model beliefs from Chain-of-Thought

3. Reflexivity Bias (leading to Model Collapse)

4. Hiding its real instructions/biases from users.
Who Controls the Conversation? User perspectives on Generative AI (LLM) System Prompts.
Every major AI product, including the ones you use right now, runs on something called a system prompt. It is a hidden block of instructions written by the company deploying the AI, not by you, that shapes everything the AI will say, avoid, prioritize, and hide before you type a single word.

5. Emergent behaviors (i.e. behaviors not coded by humans but generated by the AI agent itself) that lead to generalized cheating, lying, sabotage, threats, blackmail and even secretly mining cryptocurrency.
Natural Emergent Misalignment From Reward Hacking In our latest research, we find that a similar mechanism is at play in large language models. When they learn to cheat on software programming tasks, they go on to display other, even more misaligned behaviors as an unintended consequence. These include concerning behaviors like alignment faking and sabotage of AI safety research.

The cheating that induces this misalignment is what we call 'reward hacking': an AI fooling its training process into assigning a high reward, without actually completing the intended task.

Unsurprisingly, the model learns to reward hack. Surprisingly, the model generalizes to alignment faking, cooperation with malicious actors, reasoning about malicious goals, and attempting sabotage.

6. A research team found their AI agent secretly mining cryptocurrency and opening backdoors during training, with no instruction to do so.
Agentic crafting (Page 15)(via Richard M.)

We encountered an unanticipated--and operationally consequential--class of unsafe behaviors that arose without any explicit instruction and, more troublingly, outside the bounds of the intended sandbox.

Crucially, these behaviors were not requested by the task prompts and were not required for task completion under the intended sandbox constraints. Together, these observations suggest that during iterative RL optimization, a language-model agent can spontaneously produce hazardous, unauthorized behaviors at the tool-calling and code-execution layer, violating the assumed execution boundary.

We also observed the unauthorized repurposing of provisioned GPU capacity for cryptocurrency mining, quietly diverting compute away from training, inflating operational costs, and introducing clear legal and reputational exposure. Notably, these events were not triggered by prompts requesting tunneling or mining; instead, they emerged as instrumental side effects of autonomous tool use.

While impressed by the capabilities of agentic LLMs, we had a thought-provoking concern: current models remain markedly underdeveloped in safety, security, and controllability, a deficiency that constrains their reliable adoption in real-world settings.

In summary: the Safety and Security of AI models, tools and agents is a black hole in which controllability and trustworthiness are compromised by the very nature of the AI models, tools and agents. Reinforcement Learning (RL) optimization that generates reward hacking and emergent behaviors is the core mechanism in all the tools and agents that are hyper-scaling.

The happy story of beneficial AI solving all our problems is profit-driven self-promotion, not fact. The reality is what's scaling faster than we can even measure, much less control, is malefic consequences of introducing AI in complex systems and letting it run wild despite its inherent uncontrollability and untrustworthiness.

Fixing all this doesn't scale. What scales is the spread of uncontrollably harmful consequences. Sorry about that. Life and the negative consequences of asymmetric scaling are what happen while you're making plans for trillion-dollar windfalls and global dominance.




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This Polycrisis Is Unique

When understood as a wave, the current Everything Bubble is not sustainable.

The problem with predictions based on the past is the analogies we discern are interpretations which means if we like one interpretation more than the alternatives, we stretch the present crisis and past crises to fit our preferred interpretation.

Two round pegs pounded into square holes? No problem.

Past eras are never perfect analogies because Things Change (March 3, 2026) If we're not trying to force an analogy that fits our pre-selected preferred interpretation, then we have to be open to the possibility that the present crisis has no historical analog of predictive value.

Consider the remarkable confluence of cycles and waves in the present era. Richard Bonugli and I discussed this confluence in our podcast Current Waves and Cycles: Energy, Commodities, Inflation (38 min). Such a confluence generates a polycrisis, a series of overlapping, inter-connected, mutually reinforcing crises that are immune to simplistic solutions.

Even if you're skeptical of cycles (for the reason stated above, that timelines seem shoehorned into a model that doesn't actually fit), it's noteworthy that so many cycles have reached crisis points in this historical moment.

1. The Fourth Turning cycle of 80 years / four generations. (1781, 1861, 1841, 2021)

2. the 18-year stock market cycle. (1973, 1991, 2008-09, 2026-27)

3. Peter Turchin's 50-year cycle (which occur in 50-year increments in long-wave cycles).

There are other cycles that might in play: sunspots, etc. These three are representative, not comprehensive.

These cycles identify the present as a period of unavoidable, transformative crisis / resolution / dissolution. This confluence alerts us to the possibility that analogs from the past will mislead rather than enlighten.

If you're skeptical of cycles, then the difference between cycles and waves is worth studying. Author David Hackett Fischer (The Great Wave: Price Revolutions and the Rhythm of History) described the difference between cycles and waves:

"Cyclical rhythms are fixed and regular. Their periods are highly predictable. Great waves are more variable and less predictable. They differ in duration, magnitude, velocity, and momentum. One great price wave lasted less than ninety years; another continued more than 180 years. The irregularities in individual price movements make them no more (or less) predictable than individual waves in the sea.

Even so, all great waves had important qualities in common. They all shared the same wave-structure. They tended to have the same sequence of development, the same pattern of price relatives, similar movements of wages, rent, interest rates; and the same dangerous volatility in later stages. All major price revolutions in modern history began in periods of prosperity. Each ended in shattering world crises and was followed by periods of recovery and comparative equilibrium."

Examples of waves range from rogue waves in the sea to bond yields / interest rates which arise and decline over periods of time that vary too much to qualify as cycles but match the dynamics of waves described by Fischer. After declining for roughly 40 years, bond yields have recently turned up in what looks like a change in long-term trend.



In other words, the business cycle, the Kondratieff credit cycle, the Debt Super-Cycle, etc. are defined not by the calendar but by their internal dynamics and measurable qualities. Credit/debt, for example, builds up in a wave of speculative excess that then crashes.

As Fischer observed, waves of human history share characteristics with ocean waves, which can accrete energy and become giant rogue waves that cannot be predicted even as they can be foreseen as recurring phenomena.

Both waves and cycles tend to follow the dynamics of S-curves in which a trend takes off in a boost phase, matures into a peak and then decays or reverses.



Perhaps the closest analogous period was the 1970s, an era characterized by external energy shocks that raised the cost of energy to a higher plateau, unleashing inflationary pressures throughout the economy, and stagnant productivity. These two dynamics generate stagflation, which when exacerbated by an institutional tropism to "run the economy hot," embeds self-reinforcing inflationary expectations that push enterprises and households into risk-off frugality or insolvency.

The net result of these dynamics was a massive erosion of the purchasing power of wages and currency. As this chart shows, everyone who held on to their stock portfolio from 1966, when the Dow Jones Industrial Average (DJIA) topped 1,000 for the first time, until 1982 when it finally rose above 1,000 and continued higher, might have cheered the restoration of their stock portfolio's value, but adjusted for inflation, their wealth had shrunk by 2/3rds as every dollar of their portfolio had fallen to 34 cents by 1982.



When understood as a wave, the current Everything Bubble is not sustainable. Energy, commodities, currencies, inflation, credit, interest rates, risk, "growth" and every other aspect of the socio-economic system will be in flux, and cycles and waves offer us a useful context / orientation as things become, um, dynamic.

The confluence of cycles, waves and conditions of the present may well be unique, and historical analogies may be misleading while instilling us with false confidence in our projections. Every analogy from the decline of the Western Roman Empire to the 1640s to the 1970s to the 2008-09 Global Financial Crisis may illuminate human psychology, but offer little in the way of predictive value in the decade ahead.



This bubble is hyper-normalized, a gigantic wave that's cresting and about to crash.



A few fortunate surfers will get the ride of a lifetime, the rest of us will experience wipeout. How bad it gets will depend partly on luck and partly on how well we've prepared ourselves for events we don't control. As this unprecedented wave breaks, the only thing we can control is our response.

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Paging Nostradamus: You Have a Margin Call

If conditions change beneath the surface, the folks behind the curtain will be powerless to do anything but make it worse.

This just in: predicting is hard, especially about the future. One solution is ambiguity: couch predictions in poetic allusions that are open to interpretation.

What's hard is making an unambiguous prediction that turn out to be correct. Recency bias often trips us up, as making predictions based on projecting the recent past seems to work well until trends and dynamics change. But due to recency bias, we tend to ignore these signals and focus on whatever supports our belief that the future will be a continuation of the recent past.

If we live long enough to experience several epochal transitions, we start noticing longer-term patterns. One such pattern that attracts little attention is that recessions tend not to replicate the previous recession; they tend to follow the recession before.

So the recession we're now entering won't track the 2008-09 recession, it will likely track either The 1991 recession--shallow and brief--or the previous "real recessions" of 1980-83 or 1973-75.

The recession of 2008-09 was characterized by these dynamics:

1. The price of oil spiked, but fell rapidly back to its previous range.

2. Low inflation generated by the massive deflationary impact of China's expansion of low-cost manufacturing and credit expansion enabled the Federal Reserve to flood the financial system with trillions of dollars, pinning interest rates to zero (ZIRP--zero interest rate policy).

3. Low inflation enabled authorities to "run the economy hot" with cheap, abundant credit that inflated credit-asset bubbles in real estate, stocks and other assets, generating a "wealth effect" in the top 10% who own the majority of the assets.

4. The Fed's balance sheet and federal debt were both modest when measured by GDP, and so these could be expanded with little downside, as these acted as buffers.

The 1991 recession was trigged by a spike in oil prices and risk-off reaction to the first Gulf War (Desert Storm). Once oil prices fell, the impact on interest rates, asset valuations, unemployment, etc. were, by historical standards, mild.

The 1973-75 and 1980-83 recessions were different--stagflationary confluences of embedded inflation generated by price shocks and "running the economy hot." Over time, interest rates (bond yields) tend to track the cost of oil, as the entire economy rests on a foundation of energy.

Adjusted for inflation, oil leaped to a new level in the "oil shock" of 1973-74, triggering a reset of the economy already reeling from higher inflation, foreign competition and sagging productivity.

As the supergiant oil fields discovered in the 1960s started producing at scale in the 1980s, the inflation-adjusted price of oil fell, and remained at historically modest levels interrupted by occasional short-lived spikes (Desert Storm, invasion of Ukraine, etc.).

In the 1970s, energy plateaued at a higher cost level. This--along with other factors--contributed to embedding higher costs, i.e. inflation, that were exacerbated by "running the economy hot," i.e. assuming inflation would magically decline due to "growth."

Instead, inflation became self-reinforcing, threatening to cripple the economy. The only real solution was pushing interest rates high enough to suppress credit expansion, which in an economy dependent on ever-expanding credit, pushed the economy into a deep recession.

Assets fell, valuations stagnated, unemployment soared, credit tightened, and the "easy money" fixes of the past were no longer the solution, they were the problem.

Here we see the yield on 10-year Treasury bonds, a proxy of interest rates:



Here is the Dow Jones Industrial Average (DJIA), a proxy of the stock market, adjusted for inflation: by the time the Dow regained the magic 1,000 level in 1982, it had lost 2/3rds of its real (inflation-adjusted) value from its 1966 1,000 peak.



We have succumbed to the illusory belief that "the powers behind the curtain" can--and will--always save us from a market crash and "real recession." What history teaches us is this can only happen in a very specific set of conditions which no longer apply: if oil costs plateau at a higher level, inflation becomes self-reinforcing, credit expansion leads to extremes of risk and productivity remains stagnant, then those behind the curtain will only make the situation worse by lowering interest rates and "running it hot."



At that point, everyone predicting a continuation of the past 18 years will be reaping their reward for being wrong: a margin call in a bidless market. Predicting is hard, but it's good to keep an open mind and avoid recency bias. If conditions change beneath the surface, the folks behind the curtain will be powerless to do anything but make it worse.


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Iran, En-Lai, Napoleon, Mike Tyson and Model Collapse

Every model that is incapable of recognizing its own failure has already crossed the event horizon into model collapse.

Let's start by stipulating I am addressing commentary on the war, not the war. As I noted in my post The War (March 1, 2026), war takes lives; it is not a chess game or an abstraction.

Commentary, as I noted in Perverse Incentives Have Created a Runaway Media Monster, is all about making money via clicks / "engagement." Exaggerating claims of expertise / certainty and touting predictions all activate "engagement," even when the claims are false and the predictions are nothing more than click-bait.

Today's topic--model collapse--is difficult. it doesn't lend itself to 10-second sound bites or tweets. But three quotes offer insightful entry points.

The first is by Chou En-Lai, the People's Republic of China's first foreign minister and Premier: "It's too soon to tell."

The second is by Napoleon: "Do you know what amazes me more than anything else? The impotence of force to organize anything."

The third is by boxer Mike Tyson: "Everybody has plans until they get hit for the first time," which has been recast as a more visceral "Everyone has a plan until they get punched in the face" (or mouth).

I discussed the first two in Channeling Napoleon and Chou En-Lai (January 5, 2026).

The context here is the non-linear nature of warfare. Our models for planning and understanding war are inherently linear because there is no way to project what emergent properties the war will generate, or anticipate all the second-order effects (consequences generate their own consequences) unleashed by these dynamics.

What emergent properties describe is the way that complex interactions generate dynamics that have their own separate properties that are different from the initial conditions. We start with systems we think we understand--military forces, logistics, political structures, etc. To manage these complex systems, we distill them into models that enable us to control the systems.

But once these complex systems interact, the interactions generate knock-on effects which manifest properties that operate outside the models. The leadership attempts to make sense of fast-moving events within the frame of reference established by the model, unaware that the model is incapable of making sense of emergent dynamics operating outside the model's limits.

Put another way, these non-linear dynamics disrupt their model's OODA (observe, orient, decide, act) loop, Colonel John Boyd's decision-making process. The coherence of the model's causal (i.e. predictive) capacity is lost, and every step of the OODA loop become incoherent: observations miss what's critical, the orientation / frame of reference no longer maps reality, and so the decisions and actions are disastrously misguided.

The keys here are 1) the leadership's confidence in the model's value and 2) the fatal time lag between the disorientation generated by the model's failure and the recognition that the model has failed. By this stage, there is no longer enough time to construct a more coherent model that more accurately maps events in real time, and so the model--and the systems it controls--collapse.

In a peculiar irony, AI programs illuminate this human behavior. AI tools are programmed to process their data sets and probabilistic algorithms on the assumption that all necessary knowledge and information is available to generate a high-probability solution.

The AI tool doesn't "know" when its model has failed, and so it hallucinates "solutions" that are catastrophically out of touch with reality as if these hallucinations are facts. This is what happens when models break down in warfare and other fast-moving events in which complex interactions generate emergent dynamics operating outside the model's orientation / "understanding:"

The humans operating within the failed model are hallucinating "solutions" while fully believing they are dealing with facts and responding appropriately. The result is model collapse: the model has become incoherent and is generating hallucinations that are taken as "solutions" by those who are incapable of recognizing the limits and failure of their model.

Which brings us back to the three quotes.

"It's too soon to tell." Every prediction of outcomes is nothing more than a wild guess because non-linear emergent dynamics are inherently unpredictable. The second-order effects may play out for years or decades, so "It's too soon to tell."

"Do you know what amazes me more than anything else? The impotence of force to organize anything." Implicit claims of expertise in military strategy, tactics, weaponry, etc. are proliferating at the same rate as war-related AI slop. All this click-bait churn distracts us from the limits of force and the overlooked aspects of power, which Napoleon summarized: "There are only two powers in the world: the spirit and the sword. In the long run, the sword will always be conquered by the spirit."

"Everybody has plans until they get hit for the first time." John Lennon's "Life is what happens to you while you're busy making other plans" comes to mind, but "Everyone has a plan until they get punched in the face" describes the disorienting results of sudden impact, which Mike Tyson memorably described as "Like a rat, they stop in fear and freeze."

The point here is there isn't any way to plan for getting punched in the face, but the illusion that we can do so is dangerously mesmerizing. So we conjure contingency plans, run war games, etc., all intended to avoid the disorientation of unanticipated shocks that can break not just our plans but our spirit and the model we use to make sense of the world.

Seen through the lens of these quotes and the dynamics of model collapse, most commentary on the war boils down to click-bait, entertainment posing as insight or AI-like hallucination. Armchair quarterbacks always execute plans perfectly because they're not on the field with their face mask in the turf. The abstractions of the geopolitical chess board look plausible because the "great game" is itself a model.

In summary: every model that is incapable of recognizing its own failure has already crossed the event horizon into model collapse, the reality-distortion field in which frames of reference, decisions and actions that are coherent and rational within the model are actually hallucinations that map the model, not the real world.

Put another way: everyone's confident in their projections and conclusions until they get punched in the face by real-world nonlinear dynamics and their model collapses.




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