Syz the moment

Source: Barchart

Wall ST is expecting Nvidia $NVDA to report revenue of $54.9 Billion tomorrow up from $35.1B in the same quarter last year Source: Wolf, https://lnkd.in/enK2fikS

Almost three times. And no, it doesn’t generate more profit than energy companies in the S&P 500. In fact, the combined free cash flow of this sector over the last year is about 20% higher than Nvidia’s. Tech innovation is incredible, but let’s not forget that something still has to power it. Source: Tavi Costa, Bloomberg

Thiel Macro has cut US equity holdings from about $212m to $74m and is now basically parked in Tesla, Microsoft and Apple. Source: Wall Street Engine

Source: @StockMKTNewsz on X

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

Maybe liquidating NVDA to invest in its biggest cash-incinerating client wasn't the best idea... Source. zerohedge

Will Nvidia stock price action follow the step of the number 1 cryptocurrency ? Source: The Market Ear