On Friday October 9^th Bloomberg published an article titled “Jeffries’ Chris Wood Sees ‘Massive Capital Destruction’ in AI.” His view is that the most likely long-term outcome of the AI boom is massive capital destruction in the US, with market share shifting to cheaper open-source Chinese models. Wood has mostly got it right in my opinion except for two differences. My first difference is that Chinese models are not the only open-source models available and that western open-source models are closing the gap. Secondly, while his view correctly flags the overbuild and the competitive pressure from lower-cost alternatives what it leaves out is the long history of innovative technology infrastructure bubbles and the reliable pattern that follows them which is:

excitement/mania → overinvestment → financial collapse → distressed assets → recapitalized cheap infrastructure fuels the next economic wave.

In regard to the AI bubble, I believe we are currently in the overinvestment phase with cracks emerging. Let us look at some historical examples to illustrate the pattern and see if we can glean what the future of this current bubble might look like.

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The US Railroad Bubble

In the decades after the Civil War, railroads became the transformative technology of the era. Capital, much of it European, flooded in. Between the late 1860s and early 1870s, tens of thousands of miles of track were laid, often into sparsely settled or unprofitable territory. Speculative financing, land grants, and the expectation of endless growth created a classic mania. Early projects showed strong returns, which attracted more money into later, riskier lines built far ahead of demand. When financing dried up, highlighted by the failure of Jay Cooke’s bank in marketing Northern Pacific bonds, the Panic of 1873 followed. More than 100 railroads went bankrupt in the ensuing years, banks failed, and investors were wiped out. The depression lasted years. Yet the physical tracks remained. The surviving and reorganized network became the backbone of American industrial expansion, enabling efficient movement of goods, people, and capital that powered decades of growth. The overbuilt infrastructure, once distressed and recapitalized, proved far more valuable to the broader economy than the paper losses suggested.

The 1920s Utility Bubble

The 1920s electrification boom followed a similar script. Central-station utilities expanded rapidly, and holding companies consolidated regional systems into large, leveraged empires. Electricity generation and revenues grew dramatically as the technology moved from novelty to infrastructure. Utility stocks soared and became a major part of the market. Holding companies facilitated access to capital and technical expertise, but the structures were opaque, and capacity was built aggressively. The 1929 crash and subsequent Depression exposed the leverage and accounting. Samuel Insull’s empire collapsed in spectacular fashion, and the Dow Jones Utilities Average plunged. Reforms including the Public Utility Holding Company Act of 1935 broke up the largest structures and shifted the industry toward regulated regional monopolies. What remained was a vastly expanded electrical grid and generation capacity. Widespread electrification transformed industry, homes, and productivity for the rest of the century. The bust was painful for equity and debt holders; the long-term economic payoff from cheap, abundant power was enormous.

The Dotcom Bubble

The most recent and directly analogous example is the late 1990s telecom fiber buildout during the Dotcom era. Carriers and infrastructure firms poured hundreds of billions into fiber-optic networks, switches, and related equipment, largely financed with debt and encouraged by forecasts of internet traffic doubling every few months. Capacity vastly exceeded near-term demand. After the bubble burst, market values collapsed, many telecoms went bankrupt, and bandwidth prices fell sharply. A large portion of the newly laid fiber sat “dark” or unlit. Through bankruptcies and recapitalizations, that physical infrastructure was acquired at deep discounts, often pennies on the dollar. The resulting cheap broadband made business models viable that would have been prohibitively expensive otherwise. Google, YouTube, Facebook, Apple’s smartphone ecosystem, Netflix, software-as-a-service, and cloud services all scaled on that excess capacity. Without the overbuilding and subsequent distress, Web 2.0 as we know it does not happen at the same speed or cost. The capital destruction was real but the residual infrastructure seeded the next wave of growth.

AI Bubble (Yes it is a bubble!)

I believe we are now in the AI infrastructure equivalent of the overinvestment phase. Massive capital is flowing into data centers, chips, power, and related equipment. The sheer scale of spending is turning advanced AI models into a commodity faster than many equity and debt investors appear to appreciate. Chinese open-source models are already applying downward pricing pressure, consistent with the Jefferies view. Cracks are visible. In my Substack piece “Wall Street’s AI Capex Party…It’s 2 AM & The Bar Is About to Close!”, the signals included stalled private credit funding for the buildout, cracking enterprise demand as promised productivity gains fail to materialize at scale, looming power constraints that are becoming the binding limit rather than chip supply, and open-source pricing pressure that compresses margins.

Additional recent indicators from public commentary reinforce this: circular deals in which AI companies run negative free cash flow and rely on debt investors to keep purchasing chips at elevated prices; recent analyses showing major players cannot cover contracted compute commitments under realistic revenue assumptions; and narrative shifts such as space based data centers that paper over terrestrial grid and power bottlenecks. Growth rates implied by current valuations look vulnerable once the second derivative of funding and utilization slows. It is not a question of not if the bubble bursts but when it bursts.

Credit Markets Always End Infrastructure Buildouts

Debt and credit have been central to every one of these infrastructure buildouts. In the railroad boom, bonds financed the majority of the expansion, often more than three-quarters of the capital. When those markets stopped absorbing new bond issues (e.g. Jay Cooke & Co. could no longer place Northern Pacific bonds) the credit system seized up. Then bank runs followed, the New York Stock Exchange closed for ten days, and within two years roughly a third of outstanding railroad bonds had stopped paying interest. The equity markets lagged in this period…credit markets ended the railroad party abruptly.

The 1920s utility expansion followed the same pattern on a different scale. Samuel Insull’s holding company pyramid relied on layers of debt and bank loans secured by the securities of the operating companies themselves (financial engineering). Once revenues softened and the banks refused to roll the notes in 1932, the structure collapsed into receivership. In this period the equity markets led the credit markets first with the 1929 crash then credit followed later.

In the telecom fiber boom, carriers raised hundreds of billions in bonds and used vendor financing and circular deals to keep the buildout going. Credit markets ultimately marked the end of the buildout but equities peaked in March of 2000 before credit. Credit spreads later widened sharply in 2001 and 2002, defaults clustered in the sector for years, and bondholders recovered roughly twenty cents on the dollar in many cases. When lenders stopped funding incremental capacity, the overinvestment phase ended and the financial collapse began.

The same dynamic is now visible in the AI buildout, where private credit has stalled, circular debt structures are proliferating, off balance sheet financing is growing and early signs of stress are appearing in CCC junk credit markets. Whether equity markets lead or lag this cycle remains to be seen but the case could be made that given the tremendous amount of passive equity investing the discounting mechanism of equity could lag and credit leads this time.

Bottom Line

Innovative technology capex cycles repeat themselves because human nature has not changed. Mania and excitement give way to overinvestment, which produces financial collapse and distressed assets. Those assets then become the cheap foundation for the next economic wave. Those who believe in cycles and a constant human nature may want to protect themselves accordingly and reduce exposure in the AI complex where risk-reward no longer makes sense. Then when the bust does arrive watch for the opportunities that emerge from the ashes where durable longer-term value will be created.

Bubbles are a feature of capitalism, not a bug. They destroy capital in the short run and, through the process of overbuilding and recapitalization, often leave behind the physical and technical foundation for longer-term productivity gains. The pattern is consistent throughout history…though the timing and severity may vary. The long-term outcome for the economy is usually more favorable than the peak-to-trough equity charts imply and a net positive over time.

“What has been will be again, what has been done will be done again; there is nothing new under the sun.” Ecclesiastes 1:9

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Two weeks ago I wrote “The Fed Hiked Interest Rates Into a Supply Shock: Was It The Right Move?” Where I basically outlined that the Committee had followed a 3-month T-bill signal to hike into a supply shock it cannot fix with the policy rate, while the demand side of the economy is already softer than the headline labor data admitted. Housing is rolling over, the AI complex is crowded and levered, private credit is showing stress in the dark, and the Nonfarm payroll series continues to overstate breadth. The argument was simple: hiking into that mix treats a supply shock problem as a classic overheating problem, and that the Fed would likely have to reverse course down the line. Well last week, the second week after the rate hike decision, we have indications that the credit markets are giving them quick feedback that they made a policy error. I was even shocked at the speed of these developments.

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The Disability Signal Across the Atlantic

On August 11 I wrote US Disabilities Hit an All-Time High of 37 Million In July: UP 23% Since Feb 2021. The BLS Current Population Survey printed 37,029,000 Americans 16 and over reporting a disability. That is seven million more people since February 2021, a 3-to-4 sigma break from the pre-2020 plateau that has not mean-reverted. The inflection is February 2021 with the Covid vaccine rollout not in 2020 when Covid was at its most virulent strain. Alternative explanations fail the timing and the magnitude test. The UK PIP system corroborates that US survey signal with something the American series cannot give you: medically assessed new claims broken down by body system and underlying cause.

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If you want to know whether that US survey signal is real, stop arguing about survey design and look at an administrative system that actually diagnoses people.

Go back to the UK Disabilities (PIP) Project we published at Phinance Technologies. The page is still up…use it. It was built so researchers, doctors, and ordinary citizens could see the same thing we saw in 2023. You can look at total body system new claims or by underlying cause new claims (best viewed on desktop) within a body system. You can pick absolute new claims, excess new claims, percent excess new claims and excess new claims z score. We also break it out by monthly and yearly data. Finally you can sort it by age group as well. Play with data and be horrified like we were in 2023. Interactive charts…all done by Phinance Technologies for free.

PIP is not the protagonist from Charles Dickens’s novel Great Expectations. It is the UK’s main working-age disability benefit officially known as the Personal Independence Pension program by the UK Department of Work. Claims are medically assessed. Decisions, “clearances,” as noted above are coded by body system and then by underlying cause. The positive award rate has been stable at around 40 percent, so you are not looking at a sudden collapse in standards. You are looking at more people presenting with more illness…new claims, not just the stock of existing claimants, sorted monthly or yearly and by age band versus a 2016–2019 trend.

That is the advantage over the US survey. The BLS series tells you that disability exploded after early 2021 with the vaccine rollout and not in 2020 with the virus. PIP tells you where in the body it exploded, and it lets you watch the timing against the vaccine rollout curve on the same chart.

Two Body Systems Make the Point

Hematological (blood) disorders went off the rails. New excess clearances rose about 217 percent in 2021 and then 522 percent in 2022 while 2023 declined but still at an absurd 374 percent above trend. Over 300 percent above trend two years running. When looking at new claims on a monthly basis Hematology jumped early and hard almost coincident with the first doses. That is a regime change in medically assessed claims. I have posted those charts more than once on X in 2023. Something broke and the administrative system recorded it at a scale that alone should have produced a public-health investigation…it did not.

Musculoskeletal claims tell a different, equally inconvenient story. Monthly clearances sat near a “normal” 10,000 through 2020 and early 2021. Then, around September 2021, they jumped and stayed elevated above 18,000 a month. The rise did not arrive with the first lockdowns or the first COVID wave. It arrived after the mass rollout and into the booster period. Inflammation, joint and soft-tissue disease, the conditions that take people out of work and onto daily-living and mobility awards. The timing is not subtle.

Those two systems are not the whole file. Cardiovascular and neurological claims rose. Breast-cancer clearances showed large excesses in 2022 and 2023 with high z-scores. Different latencies, same calendar: the break is 2021, not 2020. That is why the UK file is useful. It is not one blob called “disability.” It is a set of body systems with different clocks, all accelerating after the intervention that was supposed to end the emergency.

People will say PIP is being gamed by fraud. Look at the body-system split before you buy that. A fraud wave does not preferentially light up hematology in early 2021 and musculoskeletal in late 2021 while neurological claims print 20 plus-sigma years. An awareness campaign does not move breast-cancer clearances. Two independent disability systems, two countries, both detecting the same inflection period in 2021.

The fiscal piece is already visible in Britain. Claimant counts have roughly doubled since 2019. Psychiatric disorders are now the largest single category. Spending is on a path that forces politicians to talk about “sustainability” and tighter points tests instead of asking why so many working-age bodies failed in the same window. The US version of that conversation is coming. A permanently larger disabled share of the 16-plus US population is lower labor force participation, higher absence, higher insurance cost, and more pressure on SSDI and Medicaid. You can ignore a chart. You cannot ignore the payroll.

Bottom Line

I am not a clinician. We said that on the site in 2023 and asked doctors to explain the findings…mostly crickets. The charts are still there. Pick a body system. Pick an age band. Look at the cumulative dose curve. Watch across the many body systems how in 2020 they stay close to trend and then in 2021 they leave the trend. That is the instruction. The US disability series tells you that the population got sicker after February 2021 in the US. The UK PIP file tells you which systems broke and when. Together they are the corroborating signal no one in official public health circles wants you to see. The great cover up continues into 2026 and the damage is slowly compounding.

“A matter must be established by the testimony of two or three witnesses.”
Deuteronomy 19:15

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