"NO CUTS" IS NOW MORE LIKELY THAN 25 BPS CUT IN DECEMBER
Mid-October, it was almost a done deal Source: Kalshi @Kalshi
Long-term holders are selling hard.
~815K BTC sold in the past 30 days, the highest level since Jan 2024. With demand contracting, this sell-side pressure is weighing on the price Source: cryptoquant
🔴 Stock Market Crash "Hindenburg Omen" Triggered 🚨
The Hindenburg Omen, an indicator that correctly detected the 1987 and 2008 stock market crashes, has been triggered for the 5th time over the last month 👻😱 ➡️ What is a Hindenburg Open? The Hindenburg Omen is a technical stock-market indicator that attempts to predict increased probability of a market crash. It triggers when several conditions occur at the same time on a stock exchange (usually the NYSE), such as: A high number of new 52-week highs and 52-week lows on the same day A rising 50-day moving average Worsening market breadth Other internal market divergences It’s named after the Hindenburg disaster because it is meant to signal potential “market instability.” Source: Barchart
Nvdia stock is down 10% since the SoftBank story but still gets some brokers upgrades
Source: @StockMKTNewsz on X
We have a deal!
Among the $200B Swiss investment pledges, the US representative cites pharmaceuticals, the gold industry, and even the railway sector.
Ethereum – Next Levels to Look At
Ethereum is now down 38% since the August highs! For the moment, there are no clear signs that the consolidation is over. We’ve reached the discount zone (50% Fibonacci retracement), so it’s time to watch how price reacts around key support areas: 3170 → 50% Fibonacci retracement Imbalance zone → 2636–2933 Minor support → 2114 Major swing support zone → 1385–1955 These levels could offer potential reversal setups if buyers step back in. Stay alert for signs of momentum shifting! Source: Bloomberg
From @TheEconomist thru Mo El Erian on X:
"America’s surging stockmarket has been driven, most of all, by old investors.... Americans aged 70 and above now own 39% of all stocks and mutual funds (which mostly invest in equities), almost twice as much as was common from 1989 to 2009. The trend reflects a shift in outlook. Elderly Americans’ risk tolerance has shot up."
A very interesting article by the FT >>>
Key takeaways: ➡️ 1. Jensen Huang’s Warning Isn’t Just Self-Interest Although Nvidia benefits from greater global AI investment, Huang’s claim that China may win the AI race has substantive grounding. The argument isn’t only about chips—it’s increasingly about energy. ➡️ 2. AI Progress Is Becoming Limited by Electricity, Not Chips Training frontier models consumes massive electricity. A single GPT-4–scale model can use ~463,000 MWh/year — more than 35,000 U.S. homes. As AI workloads expand, data centre electricity consumption could more than double by 2030. By 2040, data centres may consume 1,800 TWh annually, enough to power 150 million U.S. homes. Conclusion: The bottleneck is shifting from access to high-end chips to access to cheap, abundant power. ➡️ 3. China Has a Structural Energy Advantage China is rapidly expanding renewable energy capacity: Added 356 GW of new renewable energy last year (solar + wind). Solar alone grew 277 GW, far exceeding additions in the U.S. Massive government-backed projects linking industrial policy and grid expansion: Solar in Inner Mongolia Hydropower in Sichuan High-voltage lines to coastal tech hubs Local governments also subsidize electricity for Chinese tech giants (Alibaba, Tencent, ByteDance), lowering the effective cost of AI training, even with less advanced chips like Huawei’s Ascend 910B. ➡️ 4. The U.S. Faces Growing Power Constraints U.S. wholesale electricity prices near data-centre clusters are up as much as 267% over five years. Investment in large wind and solar projects is declining due to policy and regulatory uncertainty. The White House has ended subsidies for wind and solar, slowing capacity growth. Outcome: The U.S. is adding compute demand faster than energy supply. ➡️ 5. Chip Superiority Alone May Not Decide the Winner Nvidia’s H100 and Blackwell chips still outperform Chinese alternatives. But the historical “chip supremacy” model may matter less as: Chip performance grows only single digits yearly. China’s energy capacity grows double digits yearly. More cheap power → more compute hours → more model training → faster innovation. ➡️ 6. AI Dominance Will Belong to Those With Cheap Energy The article frames AI as part of a much older pattern: Britain dominated through cheap coal. The U.S. dominated through oil and hydroelectric power. Now, AI dominance will go to those who can run the most computation, not just build the best chips. ‼️ Final takeaway: The future of AI power belongs to countries that can provide abundant, inexpensive electricity — and right now, China is building that capacity faster than anyone else
