Billion-dollar property collapse highlights private credit danger

6 hours ago

Jon Adgemis declared bankruptcy with $1.8 billion in debt, largely from private credit firms, after a hotel portfolio collapse.
Private credit is a largely unregulated shadow banking industry that has grown rapidly in Australia since the Hayne royal commission.
ASIC estimates nearly $250 billion in private credit loans outstanding, posing serious risks to the economy if many sour.
The Australian property market may be entering a prolonged downturn, increasing pressure on developers heavily reliant on non-bank loans.
ASIC and the RBA have warned about private credit's lack of transparency, unrealistic valuations, and potential systemic risks.
Investors in private credit funds often misunderstand the risks, believing they have safe mortgage backing but facing high-interest, high-risk loans.
Similar collapses occurred during the global financial crisis (e.g., Bridgecorp, Fincorp), highlighting cyclical dangers.
SQM Research downgraded a Centuria Capital fund, citing uninvestable lending practices and sector transparency issues.
Price falls in Sydney and Melbourne are accelerating, squeezing developer margins and threatening half-finished projects.
Losses could cascade to self-managed super funds and private investors, with many awaiting the Adgemis liquidation hearings.

A $45 billion hedge fund run by a 24-year-old former OpenAI employee lost 67% in July after being up 439% for the year, due to aggressive AI-related investments.
The AI industry's downturn is linked to cheaper Chinese models, insufficient revenue, Trump's tariffs, and financial maneuvers, causing a market-wide crash risk.
Private credit, a $3 trillion arm of private equity, is heavily entangled in AI through loans for data centers and software companies, leading to potential mass defaults.

Richard Murphy warns stock prices could fall by 50% due to extreme valuations, AI overvaluation, and high margin debt.
The Bank of International Settlements cautions that an AI crash could trigger an investment drought, economic contraction, and even a crisis.
Current CAPE ratio is near 1929 and dot-com bubble levels, suggesting a major market correction is historically plausible.

The AI industry is at risk of a crash due to unsustainable capital expenses, circular revenues, massive debt, public pushback, corporate skepticism, diseconomies of scale, and institutional warnings.
Revenue cannot cover the huge capital investments in data centers; analysts estimate $2 trillion annual revenue needed but no credible forecasts for even half that.
The industry is supported by a cycle of investments among a few tech and chip companies, making it vulnerable to collapse.

Alphabet, Microsoft, Amazon, Meta, and Oracle have $1.35 trillion in reported debt, but a Nikkei study reveals an additional $1.65 trillion in off-balance-sheet debt, up eightfold in four years.
The hidden debt uses off-balance-sheet vehicles similar to Enron's, but is now legal due to tighter rules and fuller disclosures, though it echoes Enron's structure.
Companies package debt for chips, servers, and power into separate legal entities like joint ventures to avoid recording it on their own accounts, e.g., Meta's Hyperion data centre structure with $27 billion debt.

The article compares the AI boom to the 2008 mortgage crisis, highlighting that both rely on acceleration (second derivative) rather than levels or growth rates.
It argues that AI financing is structured like a credit-driven real estate cycle, not a technology cycle, with heavy reliance on take-or-pay contracts and debt.
OpenAI is identified as a key 'subprime' borrower, dependent on continuous equity refinancing to service massive compute commitments without profitability.

A $45 billion hedge fund run by a 24-year-old former OpenAI employee lost 67% in July after being up 439% for the year, due to aggressive AI-related investments.
The AI industry's downturn is linked to cheaper Chinese models, insufficient revenue, Trump's tariffs, and financial maneuvers, causing a market-wide crash risk.
Private credit, a $3 trillion arm of private equity, is heavily entangled in AI through loans for data centers and software companies, leading to potential mass defaults.

Richard Murphy warns stock prices could fall by 50% due to extreme valuations, AI overvaluation, and high margin debt.
The Bank of International Settlements cautions that an AI crash could trigger an investment drought, economic contraction, and even a crisis.
Current CAPE ratio is near 1929 and dot-com bubble levels, suggesting a major market correction is historically plausible.

The AI industry is at risk of a crash due to unsustainable capital expenses, circular revenues, massive debt, public pushback, corporate skepticism, diseconomies of scale, and institutional warnings.
Revenue cannot cover the huge capital investments in data centers; analysts estimate $2 trillion annual revenue needed but no credible forecasts for even half that.
The industry is supported by a cycle of investments among a few tech and chip companies, making it vulnerable to collapse.

Alphabet, Microsoft, Amazon, Meta, and Oracle have $1.35 trillion in reported debt, but a Nikkei study reveals an additional $1.65 trillion in off-balance-sheet debt, up eightfold in four years.
The hidden debt uses off-balance-sheet vehicles similar to Enron's, but is now legal due to tighter rules and fuller disclosures, though it echoes Enron's structure.
Companies package debt for chips, servers, and power into separate legal entities like joint ventures to avoid recording it on their own accounts, e.g., Meta's Hyperion data centre structure with $27 billion debt.

The article compares the AI boom to the 2008 mortgage crisis, highlighting that both rely on acceleration (second derivative) rather than levels or growth rates.
It argues that AI financing is structured like a credit-driven real estate cycle, not a technology cycle, with heavy reliance on take-or-pay contracts and debt.
OpenAI is identified as a key 'subprime' borrower, dependent on continuous equity refinancing to service massive compute commitments without profitability.

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