Скачать или смотреть $75B Dark Leverage Behind AI: The Crash Will Hit Your Portfolio Next.

There's a giant balloon hanging over the U.S. economy. It's plugged into the power grid, swallowing hundreds of billions in GPUs, data centers, and infrastructure. Everyone can see it's swollen. Everyone knows it can't keep inflating. But nobody dares let it pop. This is the AI bubble of 2025—not just a price bubble, but a resource fever where capital, credit, and electricity are being pushed to their limits faster than real economic value can catch up.

Timestamps:
0:00 - Why is the 2025 AI boom a "resource fever" that threatens your portfolio, job, and electricity bill?
2:02 - How are Big Tech companies hiding $75 billion in AI infrastructure debt off their balance sheets?
4:56 - Why is CoreWeave's $22 billion OpenAI contract built on fragile "dark leverage" through Special Purpose Vehicles?
8:14 - Why can't electricity infrastructure keep pace with AI data center demands despite unlimited capital?
9:51 - What does the productivity paradox reveal about AI's actual economic impact versus the $300-400 billion annual investment?

A significant financial bubble is inflating due to massive investments in artificial intelligence infrastructure, threatening the broader us economy. This video explores the potential for a market crash, detailing how its indirect impact could affect your portfolio, job, and cost of living. Understanding this dynamic is crucial for smart investing and managing your personal finance in these uncertain times.

Key Questions Answered in This Video
Q: What makes the AI bubble different from just high valuations?
This isn't just a price bubble—it's a resource fever where physical constraints meet financial leverage. AI companies are consuming capital, credit, and electricity faster than they can generate economic value, with Big Tech spending $300-400 billion annually to produce only $20-40 billion in AI revenue. The bubble is hitting physical limits: electricity (20+ gigawatts needed but 5-7 year grid connection times), manufacturing capacity (transformer lead times measured in years), and infrastructure that can't scale at the speed capital demands.

Q: How is the AI boom being financed, and why is that dangerous?
Free cash flow at AWS, Google, Microsoft, and Meta is down 40% while AI capex consumes 60% of their operating cash flow—the highest share in two decades. The marginal dollar of compute is now funded by complex leverage: Oracle carrying $104 billion debt with negative free cash flow, CoreWeave using SPVs to hide infrastructure debt off balance sheets, and private credit funds lending against GPU and data-center leases that are being securitized like pre-2008 mortgage CDOs. When debt is hidden in Special Purpose Vehicles and the smallest, most vulnerable players carry the largest operational risk while giants harvest the narrative, one delay or interest-rate shock can shake the entire structure.

Q: What is the productivity paradox, and what does history teach us?
The productivity paradox is when massive technology investment fails to generate immediate productivity gains—Big Tech spending 3-4% of GDP on AI while US productivity grew just 2.3% in 2024, barely above the decade average. History shows the pattern takes 10-30 years: railroads peaked at 7% of British GDP in 1846 then crashed 50%, but forty years later contributed 25% of GDP; electrification began in the 1880s but productivity gains didn't materialize until the 1920s; the dot-com bubble saw NASDAQ fall 75% and half of companies die by 2004, yet productivity gains came 15-25 years after initial 1980s IT investment. The infrastructure proves valuable eventually, but original investors at peak hype get destroyed on timing.

Q: If the AI bubble breaks, how does it affect ordinary people beyond tech stocks?
The damage transmits through four channels that hit your daily life. First, GDP impact: data-center build-outs now drive US growth through construction jobs and equipment orders, so project cancellations could cost 0.5-1 percentage points of GDP in quarters, especially in Texas, Arizona, Utah, and Ohio banking on AI campuses. Second, credit contagion: highly leveraged players like Oracle and CoreWeave stacking tens of billions in AI debt will feed into wider credit spreads when revenue disappoints, tightening financing across sectors. Third, private credit exposure: funds heavily exposed to SPVs backed by GPUs are securitizing data-center leases into asset-backed securities similar to pre-2008 mortgage CDOs, except the collateral is compute demand. Fourth, your electricity bill: if data centers get built but AI revenue doesn't materialize, utilities still recover grid upgrade costs by passing them to ratepayers—you pay for overbuilt infrastructure whether AI succeeds or not.