Ben Hunt on World War AI. How's that whole golden age thing going for you so far? That golden age of human leisure and wealth awaiting us in a world optimized for the thinking machines. Are you working a bit less today, enjoying the early fruits of all this 'AI productivity'? Or are you somehow working longer, more stressful hours than ever?

I don't see how the carrot was ever going to work. It's just too at-odds with our actual lived experience, even here in Fiat World where our reality is declared and announced to us. They're going to need the stick. They're going to need to tell us that national survival is at stake, that our enemies will triumph if we don't make the 'necessary sacrifices' to win this 'AI arms race'.
They're going to need a war.

Oh, maybe not an actual war, but the functional equivalent thereof, full of threats real and imagined and adversaries foreign and domestic. They're going to need World War AI. (Moral Equivalent of War - MEoW)

Perscient's semantic signature tracking narratives that big AI capex is needed to compete with China rose by 0.83 from the previous week to reach an all-time high z-score of 3.96, reflecting unprecedented narrative density around this justification for infrastructure spending.
The Techno-Oligarchs know what they're doing. They've got their Wall Street Renfields to dangle the bags of money and their Washington stooges to play the patriotic duty song. You will know them by their fruits...

Bah! Yes, China is our geopolitical adversary. No, this is not an AI arms race or an AI war.

First we see through the stories we are told. Then we take the fight to our true enemy.
Clear eyes, full hearts, can't lose. ((2018-10-02) Hunt Clear Eyes Full Hearts Can't Lose)

The United States spent $296 billion over a roughly four-year period to fight World War II, which would translate to about $4 trillion in today's dollars.

At its peak (1943), the war effort accounted for 37% of US GDP, and no aspect of American life was untouched or unconstrained by the US government's reallocation of the three basic building blocks of economic activity -- labor, capital and energy (energy being my shorthand for all physical resources as well as the core input to mining, farming, manufacturing and transportation) -- and the enormous expansion of government's role in American society to carry out this reallocation. In particular, every aspect of consumer behavior was subordinated to the political will required to execute the war effort, a political will which created extreme shortages in the labor, capital and physical resources available to the consumer economy.

I think it's hard for Americans today to grasp both the level of consumer sacrifice that was required during World War II and the level of government propaganda 'nudge' involved in enforcing that consumer sacrifice.

These were hard times! Shortages of food, energy and labor created extreme cost-push inflation, like our Covid-era supply chain inflation but on steroids, to which the government responded with draconian price controls on EVERYTHING. And when price controls didn't work, meaning that when even a suppressed market failed to distribute enough calories to enough people to prevent widespread hunger if not starvation, the government abandoned market mechanisms altogether and instituted outright rationing on food, energy and other necessities.

At the same time, every bit of available domestic investment capital and savings (which are the same thing) was absorbed by the federal government and unavailable for the consumer economy. That meant that in addition to the extreme inflationary pressures from widespread shortages, there was ZERO economic growth from small and medium businesses, which were an even larger portion of American GDP back then than they are today. The only thing that kept the American economy from collapsing into a stagflationary disaster was the $4 trillion that the US government spent on manufacturing war materiel and -- hold this thought! -- the enormous number of new jobs created from that.

The same amount of inflation-adjusted money we spent on World War II -- somewhere between $4 trillion and $5 trillion -- is scheduled to be spent on AI and datacenter buildouts in the United States over the next four years.
Yes, our economy is proportionally bigger today, so this is 'only' something like 15% of US GDP ($30 trillion in 2025), but an economic mobilization of this magnitude will require a similarly massive reallocation of our fundamental economic building blocks -- labor, capital and energy -- especially capital and energy.

On the capital side, it's difficult to communicate how much money this is over such a short period of time. As JPMorgan puts it in their magisterial research note on AI Capex financing, "The question is not which market will finance the AI-boom. Rather, the question is how will financings be structured to access every capital market.” Here's their chart for where they think the money will come from.

I'd call your attention in the $1.4 trillion attributed to "Need for Alternative Capital / Governments", which combines both our favorite financial topic du jour -- private credit -- with direct government subsidy/investment.

This is the necessary context for understanding OpenAI CFO Sarah Friar's recent comments at a Wall Street Journal conference that the company would 'welcome' a federal government 'backstop' on private debt financings of this datacenter buildout, as well as Sam Altman's unintentionally hilarious 5,000 word tweet to 'clarify' Friar's very clear and very correct and very intentional words.

Sarah Friar didn't 'misspeak' when she called for a federal backstop -- by which everyone means and intends a US Treasury guarantee (too big to fail) -- on AI datacenter debt issuance, and she didn't need to 'phrase things more clearly'. She used exactly the right word to describe exactly the policy that OpenAI and Wall Street and every other participant in this $10 trillion ouroboros ecosystem desperately wants and frankly requires for this massive reallocation of capital to have a chance of succeeding.

we're a good trillion dollars short in the funding required to make this work here in the US. All from additional borrowing and deficit spending, of course, just like in World War II when the federal debt skyrocketed to an amount that was 100% of GDP. What's different today, of course, is that the federal deficit is already at World War II debt-to-GDP levels before the additional borrowing for the AI buildout support. Bottom line: whatever you think the future path of US debt-to-GDP looks like, you're too low.

The economic term for the impact of capital reallocation at this enormous scale is 'crowding out'. The public and private capital that is invested in or lent to the AI hyperscalers and their counterparties over the next four years is that much less public and private capital available to be invested in or lent to the rest of the economy.

The inevitable impact of a massive reallocation of capital away from the consumer economy is that consumer credit becomes more expensive (if it's available at all), capital-intensive consumer services like health insurance and homeowners insurance become more expensive (if they're available at all), consumers stop spending (especially the bottom 50%), and consumer-facing businesses stop hiring (if they're not actively cutting back).

This is all happening already.

Credit applications regardless of type (credit cards, auto loans, mortgages, mortgage refis) are being rejected at the highest rate since they started the survey. Mortgage refi applications (so these are not the poors, these are middle class and up consumers who own their home) are being rejected at an especially high rate, north of 45%.

As for consumer spending ...

These charts show the drivers of US GDP growth for the first half of the year. The chart on the left shows that spending on tech accounted for almost half of US GDP growth in the first half of the year. The chart on the right shows that growth in tech spending (which is about 5-6% of the US economy) accounted for more GDP growth than all consumer spending (which is about 60% of the US economy).

What I've described so far is what happens with a war-footing reallocation of capital. The impact of a war-footing reallocation of energy is even harder on the consumer economy.

I'll turn to my friends at JPMorgan to set the stage. This is projected datacenter electricity consumption globally (of which the US is the largest portion, but less so than in projected capital spending).

What you're seeing is annual terawatt-hours (TWh) of electricity consumed, which is the Godzilla-version of the same sort of kilowatt-hours information you'd get on your utility bill. I really like starting with the consumption projection

JPMorgan is estimating that datacenters (globally) will consume about 1,100 TWh of electricity in 2028. My guess is that 70% of that electricity consumption will come from US datacenters, up from 60% in 2023 as the new construction will be predominantly in the US.

To level-set a little bit, in 2023 the entire US economy consumed approximately 4,000 TWh, per the US Dept. of Energy. Of that total, datacenters consumed 175 TWh, or right at 4.4% of the entire economy.

Now in a good year, the United States grows its total electricity consumption by 2-3%, because electricity consumption is an effective proxy for GDP growth. So let's be generous and take non-datacenter electricity consumption and grow it by 2% per year, and see where we end up in terms of relative power consumption between datacenters and the rest of the US economy.

With 2% electricity consumption growth (i.e. economic growth) in the non-datacenter economy, datacenters will have a 15.9% electricity consumption share of 5,023 TWh in 2028, 18.8% share of 5,308 TWh in 2029, and 22.1% share of 5,644 TWh in 2030.

The datacenter slice of the US electricity consumption pie is growing much larger and much faster than the pie itself can possibly grow.
Datacenters go from a rounding error in 2024 to consuming close to one-quarter of our electricity by 2030.
It is impossible to build enough new power generation supply to support both projected datacenter growth and growth in the rest of the US economy.

Ignore interconnects and transmission and all that stuff. Ignore the 3-year wait for new gas turbines from Cat or GE. Ignore all of the wind and solar projects that this administration has killed. Pretend -- and it's totally pretend -- that we can add 100 gigawatts or whatever the number is of new power generation to support the required electricity demands of the AI 'war effort'. It still doesn't work.

That deficit must be rationed to the rest of the economy, first by price -- which is why your utility bill has gone up so much already -- and then by allocation, i.e. scheduled brownouts and price controls.

The kicker, of course, is that higher electricity prices filter into everything. This IS cost-push inflation, and the same story arc from World War II

But here's what's different. Unlike World War II, there will be no net new jobs created by World War AI.

Most of the readers of this note work for a non-tech company that is 'trying to figure out how to use AI'. Let me guess how that's going for you. At the vague direction of your board or CEO or EVP, your company has spent a not-small amount of money on AI 'projects'

This has been going on for about two years now. There have been some modest process improvements

but you haven't seen much impact from these AI projects on your top-line revenue growth.

it's not going to open up new revenue opportunities or create new products and services. That's just not what inference does.

In fact, the best way to understand World War AI is that the 'war aims' are not directed at another country, no matter what you may hear about China, but are directed at human labor itself. The goal here -- not the unfortunate side effect but the intentional goal! -- is to eliminate much of human labor, both white collar / symbolic manipulation jobs today with AI virtual agents and blue collar / physical manipulation jobs tomorrow with AI-supported robots.

So what do we DO about it?

First we refuse to be a useful idiot for the Techno-Oligarchs. We maintain a critical distance when these messages of World War AI wash over us by asking: Why am I reading this now? We understand the messages for what they are: an intentional effort to hijack our autonomy of mind and our collective political will.

We fight back, not by a frontal assault on the Techno-Oligarchs and their billions and their well-established power centers on Wall Street and in Washington, not by being against 'technology' or 'progress' or 'national security', but by being FOR three policies that support the American people over the Techno-Oligarchs' machines.

1) We are FOR the rapid domestic reshoring of manufacturing of all types.

Yes, Apple should absolutely be pressured/required to bring significant pieces of iPhone manufacturing back to the United States, as should every behemoth American company that has offshored vast swaths of its operations. Will this hurt their margins? Sure, couldn’t care less. I'm dubious about how possible this is - Apple doesn't own those factories, and it's a whole ecosystem, and skills we've lost....

Trump is totally right to make reshoring a cornerstone of his economic policy, and it’s beyond pathetic that his predecessors for the past 25 years -- in both parties! -- have done everything possible to accommodate wanton offshoring.

Reshored manufacturing facilities are ‘good’ economic growth drivers and ‘good’ electricity consumers, meaning that they generate a broad ecosystem of jobs and small/medium enterprise entrepreneurialism. This is the economic activity that deserves a bigger slice of the energy pie!

2) We are FOR the rapid buildout of power generation projects from all sources.

Power generation growth IS economic growth. Yes, we need wind and solar. Yes, we need nuclear. Yes, we need gas. Yes, we need coal. Do I care about carbon? Of course I care about carbon. I care more about the Blade Runner future we are creating in this country, with a permanent underclass and a fascist techno-oligarchy.

Build it ALL, and use every possible government carrot and stick to build it all NOW, because massive energy production over the next three years is the only path out of this mess that doesn’t end up with either the AI authoritarianism of Proconsul Vance or the AI backlash of Comrade Mamdani.

the key to abundance is energy. Period. Full stop. If we achieve abundance in energy, abundance in every other economic and social good -- housing, jobs, incomes, healthcare, education, you name it -- will follow.

3) We are FOR a hard allocation limit of 10% of a state's electricity generation capacity to datacenters.

This is the big one. The other two policies will get at least lip-service acquiescence from the Techno-Oligarch stooges, but this one will be fiercely fought because it actually bites. It actually contains them.
The problem to solve isn’t just how to grow the power generation pie, it’s how to prevent AI datacenters from taking an insanely large slice of that pie.

The point of this consumption cap isn’t to prevent AI ecosystem growth. It’s to prevent AI ecosystem growth from swallowing up every watt of available energy and starving the rest of the American economy.

The beauty of a 10% cap is that it doesn’t bite them yet. I’m pretty sure that datacenter power consumption in every state is currently under 10%, so while it dramatically diminishes the economics of future projects -- which is the whole point, because this is what reduces the over/misallocation of capital to datacenters! -- it’s not taking anything away from current players.

See also his Nov12 video "AI and AI Capex Webinar" where he lays out 3 scenarios

• AI Authoritarianism: the GenAI folks suck up the resources, and consumers get brownouts and shortages. (Proconsul Vance) Update: see Genesis Mission for next steps in this.
• AI Subordination: rein-in capex to "sustainable" amounts (resulting in significant losses in AI stocks, but things keep moving)
• AI Backlash (from populists) (Comrade Mamdani): late but big backlash

MyComments - Oct14'2026

• what's the reality-context or theory of change behind these policy preferences? Are they triangulated on what's realistic?
• what if we capped %-of-energy without greenlighting fossil-fuel increase?
• I made a couple Gemini pages related to this
  • Jobs, Offshoring, and Economic Transition
  • Apple Workforce and Reshoring

Google Gemini on ways that past bubble crowding-out has been controlled (Oct14'2026):

To counter the crowding-out of capital and energy by massive structural booms, economists, policymakers, and historians look to similar historical episodes. Whether dealing with the 19th-century railway manias, the mid-20th-century rural electrification, or wartime industrial mobilizations, alternative frameworks focus on isolating the boom's footprint, forcing self-funding, and ring-fencing public utilities.

1. Capital & Finance Isolation Strategies

When speculative or infrastructure booms threaten to dry up credit for traditional consumer and small-business sectors, past precedents offer several ways to shield the rest of the economy:

• Forced Internal Financing (The Railway Mania Precedent): During Britain's 1840s "Railway Mania," speculative capital rushed into rail infrastructure, starving other industries of cash. Economists and regulators eventually pushed for strict "calls" on shares—forcing companies to rely strictly on staged equity calls from their original investors rather than letting them siphon short-term credit from commercial banks or asking for government-backed debt liquidity.
• Segmented Credit Markets: Rather than letting a single mega-sector set the market price for all credit, governments have historically used differential interest rates or credit steering. For example, during post-WWII reconstructions, central banks used quantitative controls to ensure commercial banks maintained a mandatory quota of low-interest loans for agriculture and small businesses, preventing heavy industry or state infrastructure from consuming the entire loanable funds pie.
• Banning Sovereign Guarantees: A primary line of defense against capital crowding is firmly rejecting corporate requests for public backstops. Historically, when private entities shoulder 100% of the downside risk without a Treasury safety net, the pace of capital allocation naturally slows down to match real market capacity, preventing an artificial credit freeze in consumer markets.

2. Energy & Utility Capacity Protection

When a single emergent industry threatens to monopolize physical infrastructure or energy grids, historical policy shifts focus on structural separation:

+-------------------------------------------------------------------------+

| STRUCTURAL ENERGY ISOLATION MODELS |
+-------------------------------------------------------------------------+

| 1. "Behind-the-Meter" Co-Location | Speculative clusters must build |
| | dedicated, isolated power plants |
| | without tapping the public grid. |
+-------------------------------------+-----------------------------------+

| 2. Interruptible Rate Contracts | Mega-consumers are automatically |
| | disconnected from the grid during |
| | periods of peak civilian demand. |
+-------------------------------------+-----------------------------------+

| 3. Utility Decoupling Rules | Legal firewalls prevent industrial|
| | infrastructure costs from inflating|
| | everyday consumer utility bills. |
+-------------------------------------+-----------------------------------+

• Mandatory "Behind-the-Meter" Self-Generation: Instead of capping energy by an arbitrary percentage, some energy economists advocate for absolute grid decoupling. Under this model, heavy industrial or computational clusters are prohibited from drawing from the public transmission grid entirely. If they want to expand, they must contract, build, and isolate their own dedicated power sources (such as an on-site nuclear or natural gas plant) "behind the meter."
• Interruptible Load Contracts (The Heavy Industry Precedent): Historically used for aluminum smelters and heavy manufacturing, utilities employ Demand Response and Interruptible Rates. In exchange for cheap baseline power, these mega-users sign contracts allowing the grid operator to completely shut off their power with zero notice during peak residential demand, ensuring civilian capacity is never crowded out during heatwaves or winters.
• Utility Rate Decoupling and Infrastructure Ring-Fencing: To prevent cost-push inflation from filtering into consumer bills, regulatory frameworks can legally separate the cost of building new high-voltage transmission lines. If an expansion is driven strictly by a specific sector's demands, utility boards can mandate that 100% of those infrastructure costs be paid via specialized industrial tariffs, legally preventing those capital expenses from being factored into the baseline rate paid by residential consumers.

If you'd like to explore further, let me know if you want to look into:

• How specific historical periods (like the 1970s energy crisis or the canal boom) handled resource rationing
• The mechanics of credit steering policies used by central banks to protect small businesses

GoogleGemini on who the biggest AI debt-holders are (Oct14'2026)

The biggest holders of debt from AI hyperscalers (Alphabet, Amazon, Meta, Microsoft, Oracle) and frontier labs (OpenAI, Anthropic, xAI) fall into four major financial sectors. As total AI infrastructure debt expands rapidly—surpassing $1.3 trillion in visible debt and up to $1.65 trillion in off-balance-sheet commitments—the risk has shifted from traditional corporate balance sheets to a broader web of Wall Street lenders. [1, 2, 3, 4, 5, 6, 7, 8]

The primary groups holding this debt include:

1. Mainstream Institutional Bondholders (Investment-Grade Corporate Debt)

Public bond markets are the largest visible funding channel. Hyperscalers have aggressively issued investment-grade corporate bonds to fund data center expansions. [9, 10, 11]

• Who holds it: Massive asset managers, mutual funds, and pension systems (such as Vanguard, BlackRock, and state retirement funds) hold hundreds of billions in long-duration tech debt. [11, 12]
• Recent Activity: Amazon raised $53 billion via bonds, Oracle increased its long-term debt to $149 billion, and Alphabet issued a massive 100-year bond. [3, 13]

2. Major Commercial and Investment Banks (Construction & Project Finance)

Large global banks write multi-billion dollar construction loans for individual AI data center campuses. They fund the real estate and utilities before these properties are leased to tech tenants. [1, 9]

• Who holds it: According to credit tracking data, the largest active bank lenders in this space include TD Bank ($26.8B), Wells Fargo ($16.4B), JPMorgan Chase ($13.3B), Bank of America ($12.8B), and MUFG ($11.8B). [14]
• The Shift: Banks have recently begun structuring Significant Risk Transfers (SRTs) to quietly distribute and offload the riskiest slices of this data center construction debt (such as a $38 billion debt package tied to Oracle data centers) to protect their own balance sheets. [15]

3. Private Credit Funds & Private Equity (Special Purpose Vehicles & Shadow Debt)

To avoid credit downgrades, hyperscalers and frontier labs route massive amounts of financing through Special Purpose Vehicles (SPVs) and joint ventures. This debt sits "off-balance-sheet" in the footnotes of financial filings, backed by private money. [7, 8, 16]

• Who holds it: Mega-alternative asset managers like Blue Owl Capital, Blackstone, and Apollo Global Management. For example, Blue Owl funds own 80% of a joint venture carrying a $27 billion debt package for Meta’s Hyperion data center campus. Apollo has similarly backed multi-billion dollar chip-renting structures for Elon Musk's xAI. [2, 8, 16, 17]

4. Chip Providers & Tech Vendors (Vendor Financing)

In a unique circular loop, tech companies themselves have become major lenders to frontier labs. [2]

• Who holds it: Nvidia is actively backstopping more than $100 billion in debt for a massive Ohio data center built for OpenAI. [5]
• Cloud Credits as Debt-Equivalent: Hyperscalers like Microsoft, Amazon, and Google act as lenders to frontier labs (OpenAI and Anthropic) by providing billions in cloud-compute credits rather than pure cash, effectively holding specialized operational infrastructure debt over the labs. [18]

If you would like to explore this further, let me know if you want to look into how off-balance-sheet SPVs work in these tech deals, or look closer at the credit rating risks currently facing specific companies like Oracle or Alphabet. [13, 16]

[1] https://www.brookings.edu
[2] https://www.forbes.com
[3] https://finance.yahoo.com
[4] https://cheatsheets.davidveksler.com
[5] https://seekingalpha.com
[6] https://fortune.com
[7] https://money.usnews.com
[8] https://www.briefs.co
[9] https://www.useluminix.com
[10] https://www.mellon.com
[11] https://www.remio.ai
[12] https://corporate.vanguard.com
[13] https://finance.yahoo.com
[14] https://www.bisnow.com
[15] https://startupfortune.com
[16] https://www.linkedin.com
[17] https://www.wsj.com
[18] https://theedgemalaysia.com