Zhenya Manukyan

Jerome Powell says gold is irrelevant to monetary policy and that the Fed doesn’t “get spun up” over asset prices.
But gold is a monetary metal.
When Alan Greenspan was Fed Chair, he said gold was the most important indicator he watched. Even without a gold standard, he used gold to judge whether policy was too loose or too tight. Rising gold meant rates were too low. Falling gold meant policy was too tight.
Today, gold is above $4890 — over 10× higher than in Greenspan’s era. That’s the market screaming that monetary policy has been too loose for too long.
Powell argues that if there were a real loss of confidence, the bond market would show it. But loss of confidence appears first in the most sensitive markets — gold and silver — just like subprime cracked before the broader mortgage market in 2007.
Gold has been warning the Fed from $2,000 → $3,000 → $4,000 → $5,000. Instead of tightening, the Fed loosened.
Powell says gold is “contained.”
History says that’s how every crisis starts.
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They lose because of how they handle uncertainty.
Fear isn’t the problem. Uncertainty is.
When traders feel fear, they:
enter lateexit earlyoversize positionsfreeze under pressure
The solution isn’t confidence.
It’s structure.
Here’s a simple framework professionals use to reduce fear:
1. Define invalidation before entry
Before clicking BUY, you should know exactly what proves you wrong.
When the worst case is predefined, fear loses power.
2. Size so your nervous system stays calm
If a position keeps you anxious or unable to sleep, it’s too large.
Smaller size = clearer thinking = better decisions.
3. Test before committing
Start with a small position.
If price moves in your favor, scale in.
If it doesn’t, exit early.
You’re testing a thesis, not defending an opinion.
4. Wait for confirmation, not excitement
Beginners rush in early out of FOMO.
Experienced traders enter later — but with clarity.
5. Use rules to contain emotion
Successful traders aren’t fearless.
They operate with rules that stop emotions from making decisions.
Trading without structure turns fear into control.
Trading with structure turns fear into information.
That’s the difference between gambling
and running a process.

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The global AI rivalry between the United States and China is often framed as a race. But that metaphor may be misleading. According to analysts, what’s unfolding looks less like a sprint—and more like a decathlon.
This week, Microsoft president Brad Smith joined Nvidia CEO Jensen Huang and Elon Musk in publicly warning that the US may be losing ground to China in the AI race. Not in cutting-edge models, but where it increasingly matters: real-world adoption beyond the West.
Not One Race, but Many
American companies still dominate advanced semiconductors, cloud infrastructure, AI platforms, and talent attraction. China, however, is pulling ahead in areas that translate faster into economic and geopolitical influence—industrial robotics, deployment of AI hardware, quantum communications, and battery technologies.
Crucially, China is winning hearts and servers across the Global South.
The Power of Cheap, Open AI
Chinese firms, backed by state subsidies, are exporting low-cost open-source AI models that are highly attractive to developing economies. Models like DeepSeek R1 may not be the most advanced—but they are accessible, affordable, and deployable at scale.
For over 140 countries, China is already a larger trading partner than the US. Through infrastructure, trade, and investment, Beijing is nudging these countries toward Chinese tech standards—AI included.
Hardware: America’s Edge—and China’s Leverage
The US still holds a major advantage in computing power. Nearly half of the world’s data center capacity is American, compared to roughly a quarter in China. Nvidia’s most advanced chips remain unmatched, and Chinese alternatives like Huawei’s Ascend still lag in performance and production scale.
But there’s a catch: rare earths.
China dominates the supply chain—controlling the vast majority of rare-earth mining, processing, and magnet production. The AI hardware of the future depends on materials Beijing already owns.
Trump’s Gamble
In December, the Trump administration reversed course and lifted some restrictions on exporting Nvidia’s H200 chips to China. The logic: better to keep China dependent on American hardware than to push it into full technological self-sufficiency.
Chinese tech giants like Alibaba and ByteDance are reportedly lining up massive orders—millions of chips worth tens of billions of dollars.
Supporters say this preserves US leadership. Critics warn it may accelerate China’s ability to close the compute gap.
The Real Risk
The core question isn’t who has the best models today—but who controls the ecosystem tomorrow.
As one analyst put it:
The US may own the blueprints and the code, while China owns the factories, the hardware, and the physical infrastructure.
If that happens, the balance of economic and geopolitical power could shift far beyond artificial intelligence.
The AI race isn’t being won in a lab.It’s being won in supply chains, emerging markets, and the real world.
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