Recently, while looking at American AI stocks, I discovered a very interesting phenomenon—everyone is chasing AI themes, but few truly understand the structure of the AI industry chain. The result is blindly chasing high prices, only to get trapped later.

Actually, AI is not an industry but a complete supply chain. The profits in upstream, midstream, and downstream are completely different, and the logic behind stock price fluctuations also varies greatly.

Let's start with the upstream, which is hardware for computing power. Companies like NVIDIA, TSMC, and AMD mainly look at the supply and demand gap and prices of AI chips. When AI chips are in short supply, spot prices rise, delivery cycles lengthen, and stocks of manufacturers like NVIDIA soar. TSMC is even more critical because almost all high-end AI chips rely on TSMC’s advanced processes and CoWoS packaging. TSMC has been continuously raising prices for processes below 5 nanometers for four years since the beginning of this year, with AI chip prices increasing by 10%, and customers still rush to buy.

The midstream consists of cloud giants—Microsoft, Amazon, Google, Meta. They don’t sell chips directly but offer computing power services and model APIs. The key at this level is “whether AI services can be monetized.” Microsoft relies on Copilot and Azure, Amazon on AWS, and Google on Google Cloud. But these companies also face a problem: after investing so much money into AI infrastructure, when will they see a return? When analysts start questioning the investment return rate, midstream stocks may come under pressure. Moreover, when upstream prices surge too much, midstream costs increase, and in the short term, stock prices may also be suppressed.

The downstream is the application layer, including enterprise software companies like Salesforce, ServiceNow, and Adobe. They embed AI capabilities into their products, mainly depending on the adoption speed by enterprises and whether they are willing to pay extra for AI features. Downstream stocks usually lag 1 to 2 quarters behind upstream stocks because it takes time for AI infrastructure to be built before revenue from applications can be reflected.

Speaking of the leading AI stocks in the U.S., NVIDIA is definitely the top choice. It accounts for 80% to 90% of the AI accelerator market revenue share, and just data center GPUs generate over $100 billion annually. NVIDIA’s moat is not only in hardware but also in a software ecosystem built over more than ten years. Developers are accustomed to programming on NVIDIA platforms, making switching costs very high.

TSMC is also a must-watch. NVIDIA chips, Apple processors, AMD server chips—almost all are manufactured by TSMC. In the first quarter of 2026, TSMC’s combined revenue is expected to grow by 35% year-over-year, with high-performance computing accounting for 58%, growing at 48% annually. JPMorgan projects that TSMC’s full-year 2026 dollar-denominated revenue could grow by 35% to 40%.

Microsoft is another key player. It leads enterprise AI transformation by integrating AI into its Azure cloud platform and Copilot enterprise assistant, successfully embedding AI technology into the workflows of global enterprises. Microsoft deeply integrates Copilot into products like Windows, Office, and Teams, which have over 1 billion users worldwide. Its monetization capabilities continue to be released, and many institutions see Microsoft as the most certain beneficiary of the “enterprise AI popularization” wave.

Amazon cannot be ignored either. Through AWS, self-developed AI chips like Trainium, and deep collaboration with Anthropic, Amazon has formed a complete ecosystem. When the market re-focuses on the monetization of AI infrastructure, Amazon’s advantages are likely to be underestimated.

Meta is a representative of the application layer. The advertising delivery on Facebook and Instagram has greatly improved in precision thanks to AI optimization, directly reflecting in revenue. Meta is a successful case of “AI direct monetization”—it doesn’t need to wait for customers to use it; it is itself the largest application scenario.

If you want to participate in AI but avoid too much volatility, you can choose Microsoft, Amazon, or TSMC. These companies are financially stable, with AI being just one of their growth drivers.

To capture mainstream AI funds, focus on NVIDIA and Meta. These companies are highly tied to AI, with strong growth momentum, but also higher volatility.

As for risks, there are mainly a few. First, overvaluation—AI stocks have surged significantly over the past two years, and many companies’ stock prices already reflect years of growth expectations. Second, capital rotation—the market may shift from hardware to software, or from AI to other themes. Third, increased competition—competitors like AMD and Google’s self-developed TPU are catching up. Fourth, geopolitical risks—export controls could impact the supply chain.

In the long term, AI’s impact on human life will be comparable to the internet revolution. But in the short term, U.S. AI stocks will definitely experience volatility. The most pragmatic approach is phased investing—gradually deploying, waiting for dips, and controlling individual stock positions.

Finally, a reminder: AI concept stocks are still investable now, but you must clearly understand whether you are buying upstream hardware, midstream platforms, or downstream applications. The logic at different levels is completely different.