From Forever Wars to AI: The New Elite Wealth Extraction Model | Simon Dixon PT 1

Simon Dixon sees the Financial, Military and Technical Industrial Complexes converging. He says military operations are shifting from physical manufacturing toward technology, with AI tested in war zones and adoption accelerating during the Russia-Ukraine War. AI, in his account, adds pressure to household wealth rather than replacing the military-industrial system. Data centers require energy, while higher energy costs and interest rates can strain indebted households and businesses, leaving them more vulnerable to selling assets or taking on further debt.

Dixon argues that the financial system is positioned to benefit from that pressure because connected institutions can create money through lending and use it to acquire assets, including when prices fall. He cites estimates that 10% of households own about 88% of the stock market, and that the wealthiest 1% of those households own half its value. He points to 30-year government debt at about 5.63% and 10-year debt at about 5.3%, saying higher borrowing costs raise mortgage, credit-card and refinancing burdens. Pension contributors, he says, may see wages lag inflation and cannot generally access retirement assets early, while wealthy asset owners can borrow against holdings.

He describes asset managers and banks as benefiting from continued investment flows, government borrowing and spending, and volatile markets. Dixon says BlackRock’s assets under management rose from $14 trillion to $15.3 trillion after the Strait of Hormuz closure, while bank share prices rose by an average of about 25%. He attributes buying demand to wealthy investors’ spare funds, pension contributions and government spending, including military contracts. Banks also earn from trading, mergers and acquisitions, and business consolidation. He frames elite power less as a coordinated conspiracy than as aligned incentives: institutions can profit across different outcomes through fees, board seats, passive pension flows, private equity, hedge funds and lending.

Dixon says AI is sharply increasing productivity, potentially shifting advantage toward companies that own or adopt AI assets and away from workers who do not. Small businesses, he says, are freezing hiring and seeking more output from existing teams; AI agents could also make genuine one-person businesses possible. He cautions that job data has not yet shown mass unemployment, but predicts losses in taxi driving, trucking and other large job categories, with the scale clearer within five years. AI-driven productivity may be disinflationary even as money creation fuels inflation, he argues, while asset-price inflation is being pushed. He sees no alternative to universal basic income for people displaced by AI, and expects people without assets to become more frustrated.

The financing needs could intensify the divide between asset owners and workers. Dixon cites about $800 billion in AI-company financing needs, $1.2 trillion in imminent U.S. government borrowing and $10 trillion of debt to roll over, against roughly $7 trillion in money-market funds. He says UK yields are about 6% and Japanese yields about 3%, with government and corporate borrowers competing for funding and pushing rates upward. Highly leveraged AI companies that cannot keep raising money could fail and be bought by cash-rich firms such as Apple or Amazon. Dixon describes this as a system that socializes losses through government debt and inflation while asset holders capture gains; even cash-generating firms such as Apple and Meta may borrow because high valuations make debt attractive.

Dixon distinguishes private-bank money creation from central-bank support: in his view, banks create money when they issue loans, while central banks mainly guarantee the system and use quantitative easing under stress. He says the U.S. system depends on rolling debt over—paying interest and selling old debt to new holders—rather than repaying principal, because removing roughly $40 trillion from circulation would collapse the economy and drive stock and real-estate values toward zero. He cites about $21 trillion in the Federal Reserve system, $15 trillion in Eurodollars and $40 trillion in U.S. government debt. He traces the structure through the Dutch central-bank, stock-market and bond-market model, the Bank of England, the 1971 end of the U.S. gold standard, the 1973–75 emergence of the petrodollar and the debt-rollover mechanisms that followed.