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There’s a concept working its way through the AI industry that most traders aren’t paying enough attention to.

It’s called AI distillation, and while the name sounds technical, the idea is surprisingly simple.

Think of it like a master craftsman teaching an apprentice. Instead of building intelligence from scratch, a large AI model trains a smaller, more efficient one that can perform many of the same tasks using far fewer resources.

That process makes AI faster to deploy, less expensive to operate and much easier to scale.

To me, that’s one of the most important developments happening in AI today.

2 Countries, 2 Completely Different Strategies

What’s especially interesting is how differently the U.S. and China are approaching this opportunity.

In the U.S., much of AI remains centered around premium platforms, proprietary models and tightly controlled ecosystems developed by companies such as Anthropic, OpenAI and xAI.

China has largely taken the opposite approach.

Rather than focusing exclusively on premium systems, Chinese developers have prioritized lower-cost models that can be deployed broadly and improved rapidly.

Distillation plays directly into that strategy by reducing the computing power required to build and operate capable AI systems.

The result is two very different visions for how AI scales.

One emphasizes exclusivity and premium pricing.

The other emphasizes accessibility and cost efficiency.

Follow the Infrastructure, Not the Headlines

That difference has real implications for investors.

As AI models become smaller and less expensive to run, more businesses can afford to deploy them.

That creates opportunities for infrastructure providers that supply computing resources without operating massive hyperscale platforms.

Those companies could benefit from broader adoption as more developers enter the market.

At the same time, smaller models require fewer hardware resources. If memory demand begins to moderate as AI becomes more efficient, companies tied to memory production could face increasing pressure even while AI adoption continues expanding.

The paradox? Cheaper tech usually leads to more spending, not less. Economists call this Jevons Paradox: As a resource becomes more efficient to use, total demand for it actually skyrockets.

Lowering the cost per query doesn’t mean businesses buy less compute — it means millions of new companies can finally afford to build with AI, driving total infrastructure demand even higher.

That’s why I think this story is much bigger than a debate about which country has the better AI model.

It’s about understanding where pricing power is shifting across the entire AI ecosystem.

For now, I’m watching the infrastructure names, monitoring how the supply chain responds and paying close attention to where institutional money begins flowing.

Sometimes the biggest opportunities don’t come from the technology itself.

They come from recognizing how that technology changes the economics for everyone around it.

P.S. Want to see what my brand-new scanner is flagging before it goes public? Tap here to join Ezra’s Telegram channel for free and catch the next live signal in real-time.

To better trading,

Alex Reid
WealthPin

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*This is for informational and educational purposes only. There is inherent risk in trading, so trade at your own risk. 

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