AI Is Too Big to Fail (sibylline.dev)

A provocative take argues we’re not just in an AI “bubble” but in a wartime-style national project where market discipline has been hollowed out by geopolitical urgency and elite political alignment. The essay cites estimates that AI-linked activity drove roughly 40–90% of H1‑2025 U.S. GDP growth and ~75–80% of S&P 500 gains, and that the sector needs on the order of $320–480 billion of capex to break even this year. It highlights structural advantages for China—3,487 GW total capacity vs. the U.S.’s ~1,189 GW, planned Chinese 2025 PV additions of ~270–300 GW vs. U.S. ~32.5 GW, and China’s dominance in robotics (≈54% of global installs vs. ~6% for the U.S.)—which together mean China can train and deploy models more cheaply and couple AI to robotics at scale. The key implication: powerful tech investors and a sympathetic administration are effectively treating AI as “too big to fail,” making government stimulus, quasi‑nationalization, and large procurement or surveillance contracts likely backstops. That distorts capital allocation, concentrates upside for oligarchs while socializing downside for taxpayers, and raises systemic risk: if AI fails to deliver transformative productivity gains, the economy could face a severe reckoning—massive misallocation, rising debt burdens, inequality, and political instability. For AI/ML practitioners and policymakers, the piece is a warning that technical progress is now inseparable from geopolitics, energy and hardware capacity, and high-stakes economic policy.