
Warsh’s Fed, Hyperscaler CapEx Chaos & Trading Compute Like Oil (LIVE w/ Steve Hou)
In this live deep dive with Steve Hou (Research Lead at Silicon Data), the hosts dissect the market's reaction to Big Tech earnings and the fundamental economics of the AI infrastructure buildout. The central question is: Is the current AI investment boom built to last?
Hyperscaler Earnings: A CapEx Hangover- Growth vs. Expectations: Google and Microsoft reported explosive cloud growth (Google Cloud <100% YoY). However, their capital expenditure (CapEx) fell short of market expectations.
- Focus Shift: The stock market punished companies that didn't raise their CapEx guidance. This signals a shift in the debate from the volume of spending to the realization of tangible ROI.
- Cloud Profitability: Google's operating margins expanded from 23% to 36%, proving the cloud growth is not just top-line driven but highly profitable.
- EBITDA vs. Net Income: While data center EBITDA (e.g., CoreWeave) is massively positive, enormous depreciation expenses on expensive hardware consume a huge portion of the profits. The gap between these metrics is historically wide.
- GPU Rental Prices are Soaring: Spot prices for H100, B200, and B300 chips are rising almost vertically. This indicates a severe demand overhang despite continuous new capacity coming online.
- Token Deflation: Simultaneously, the price of tokens is falling. Bears see this as a race to the bottom and commodification. Bulls see it as the natural proliferation of intelligence as a cheap resource.
- The Circular Demand Problem: A significant portion of demand comes from AI labs themselves subleasing capacity. Is this a sustainable flywheel? Anthropic's S-1 filing provides a strong bullish data point with 88% gross margins and 8% operating margins in Q2.
- Contango vs. Backwardation: The implied forward curve for GPU rental pricing currently shows contango (rising future prices). This is unusual for compute and implies expectations of persistent scarcity rather than a glut.
- The Risk of Long-Term Contracts: Companies like CoreWeave locked in cheap long-term contracts before the recent price surge. While this guaranteed revenue, it prevents them from capitalizing on the current high prices, echoing the hedging dilemmas seen in oil and gas markets.
- Closed vs. Open Source: The debate on switching to cheaper models is more complex than just price. Despite lower costs, enterprise switching remains slow.
- Stickiness and Friction: The iPhone analogy (superior specs vs. seamless user experience and brand loyalty) highlights that intangible hurdles and compliance concerns often make switching economically unattractive, even when cheaper open-source alternatives exist.
- Low Spending Share: While many enterprises use Anthropic models, spending accounts for ~0.1% of their budget. This can be interpreted as either massive room for growth (bullish) or limited current utility (bearish).
- Fed Policy: The Fed's interest rate policy is currently a secondary factor. As long as the returns on AI investments remain high, loans will be secured despite rising rates.
- M&A Trend: Marvell's acquisition of Lumentum (optics) highlights the trend towards vertical integration in the semiconductor supply chain, seeking pricing power and scale in an environment of persistent scarcity.






