Could synchronized agents, correlated models, concentrated investment, automated trading, or an AI-capex reversal create systemic risks?
Regulators flag herding from correlated models, dependence on a few providers, and the sheer scale of AI capital expenditure as systemic scenarios — a financial-stability watchlist that barely existed two years ago.
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The capex-reversal scenario got named, traded on, and rebutted: Citrini's 2028 Global Intelligence Crisis memo moved markets, Zvi and Smith argued it confuses a distribution problem with a collapse, and the BIS made over-commitment and circular financing an official concern. July's Korean crash provided the first live case study.Evidence: 1234
Every dollar saved on headcount flowed into AI capability that made the next round of job cuts possible.
The market-moving scenario memo: AI-driven layoffs propagating into private credit, insurers, mortgages, and tax receipts.
Accepts the disruption mechanisms; argues production gains plus monetary and redistributive policy make it a distribution problem, not a collapse.
The intense competition raises the risk of firms over-committing resources to investment projects with still uncertain returns.
Official assessment: $1T+ hyperscaler capex, opaque circular financing, and interconnected exposures could produce a financing pullback and investment bust.
First cross-jurisdiction monitoring framework: third-party concentration, correlation from common models, supervisory data gaps.
it will surpass, in real terms, the residential construction boom of 2005-06, the 1990s fiber build peak year, and the 1882 railroad peak combined.
~$1.5T annual AI infrastructure spend by 2030, ~90% externally financed — dependent on credit markets and vulnerable to spread widening.
The revenue counter-case: Anthropic's annualized revenue doubling from $9B to $19B between late 2025 and March 2026.
Case study: chip earnings, leveraged retail ETFs, and the gap between required and actual AI revenue.