Author: Changan I Biteye Content Team

In November last year, Sun Yuchen posted a tweet:

If this statement is regarded as an industry judgment rather than a traffic gold sentence, looking back will reveal:

These three lines almost represent the most authentic profit paths in the AI market.

If one had bought US stocks related to storage concepts after that tweet, what would the result be today?

• Micron: +214%

• Seagate: +180%

• Western Digital: +190%

• SanDisk: +552%

This article will explore these three lines:

Why will AI first positively affect chips, then expose energy bottlenecks, and finally long-term increase storage demand? Which assets have already emerged in this round's structure?

1. Chips: The first to benefit from AI's explosion is not the narrative, but the orders

The initial excitement of AI is not at the application layer, but at the underlying computing power.

Whether it's training large models, daily reasoning, agent calls, or multimodal processing, the first step is to get the computation running, and all these computations ultimately rely on GPUs, HBM, high-speed interconnects, and advanced processes.

In other words, the growth in AI demand will not be transmitted to the later stages first, but will instead become a more direct reality:

More chips are needed, stronger chips are needed, higher bandwidth chips are needed

This is also why AI demand is first reflected in the chip sector.

Industry data has made this quite clear. According to the fiscal year 2026 estimates, NVIDIA's revenue is expected to grow by 65% year-on-year, indicating that demand for high-end computing power chips continues to be released.

🌟What assets are in this direction

Core computing power layer: Nvidia (NVDA), AMD, Broadcom (AVGO), TSMC (TSM)

Domestic computing power layer: Haiguang Information (688041.SH), Cambrian ( 688256.SH), etc. Among these, Haiguang Information is one of the representative companies in the domestic x86 server CPU field, with a projected revenue of 9.162 billion yuan in 2024, a year-on-year increase of 52.4%.

Semi-conductor equipment layer: ASML, Applied Materials (AMAT), Lam Research (LRCX). The price of lithography giant ASML's U.S. ADR has reached a historic high since the beginning of 2026, with a single-day increase of over 8% on January 2, and since the beginning of 2026, the increase has reached as high as 27%; Lam Research has an increase of 30% since the beginning of 2026; Applied Materials has an increase of 28% since the beginning of 2026, with all three major semiconductor equipment giants' stock prices significantly outpacing the S&P 500 index.

🌟Performance in the past year

The chip sector is the first to start and has seen the largest increase in this wave of AI market. Nvidia, as the leader, has seen a cumulative increase of over 1000% since the beginning of 2023. The equipment side continuously reached new highs at the beginning of 2026, remaining in a strong upward cycle. Citigroup released a research report predicting that the global semiconductor equipment sector will usher in a "Phase 2 bull market upward cycle," with the main line of chip stocks clearly falling on ASML, Lam Research, and Applied Materials in 2026.

2. Energy: After AI expands, the bottleneck shifts from chips to electricity

No matter how many chips there are, they can’t operate without electricity.

Purchasing chips is just the beginning; truly long-term running large models, data centers, and reasoning services need continuous power supply, along with additional heating and cooling loads. Traditional data centers typically have a single cabinet power of 5 to 15 kilowatts, while AI data centers have clearly risen to 50 to 100 kilowatts, where electricity consumption and heating pressure are completely on different levels. The IEA's analysis this year mentioned that electricity consumption in data centers will increase to about 945 TWh by 2030, roughly doubling the current level, with AI being the main driving force. The U.S. Department of Energy has also clearly stated that the increasing demand for electricity in data centers is putting noticeable pressure on regional power grids.

🌟What assets are in this direction

Gas turbines: GE Vernova (GEV): Gas turbine orders are booming, with total orders for 2025 reaching 59 billion dollars, backlogged orders increase to 150 billion dollars, and management raises 2026 revenue guidance to 44 to 45 billion dollars.

Independent power producers: Constellation Energy (CEG): The largest zero-carbon power operator in the U.S., with nuclear power assets directly signing long-term electricity purchase agreements with tech giants; Vistra (VST): possessing both nuclear and gas assets, with an EBITDA guidance median for 2026 improved by approximately 30% compared to 2025

Uranium resources: Cameco (CCJ): The world's largest publicly listed uranium mining company, a beneficiary in the upstream of the nuclear power restart

🌟Performance in the past year

GE Vernova’s stock price has increased by 167% over the past year. The 52-week low was 408 dollars, peaking at 1181 dollars, almost doubling in the range. Constellation Energy hit a historic high in 2025, but then retreated approximately 28% from the peak due to regulatory policy disturbances and is currently at a relatively low position. Vistra has maintained strength overall, with long-term electricity supply agreements with data centers continuing to roll out. The energy sector as a whole has been re-priced from traditional defensive positions to a core beneficiary direction of AI infrastructure.

3. Storage: The most easily overlooked direction, but will benefit in the long term

The core logic benefiting storage is very simple: AI is not a one-time call; it is essentially a system that continuously ingests, deposits, and retrieves data.

Training requires reading a large amount of data, checkpoints must be stored during the training process, and during reasoning, models and caches must be adjusted, while RAG and agents continuously need to read knowledge bases, logs, and memories.

As a result, what AI brings is not just "more data," but:

• More frequent data read/writes

• More real-time calls

• More complex management

• Greater pressure on migration and caching

Looking further, the more expensive the GPU, the less it can run idly, so the industry will increasingly focus on how to deliver data to the computing power side more quickly and more stably.

In other words, as AI develops, storage becomes more than just a "warehouse for data"; it is the data foundation ensuring the entire AI system can operate continuously.

🌟What assets are in this direction

Storage chip manufacturers: SK Hynix (000660.KS), Samsung Electronics (005930.KS), Micron Technology (MU)

NAND / SSD / HDD manufacturers: SanDisk (SNDK), Seagate (STX), Western Digital (WDC)

Domestic storage design: Zhaochi Innovation, Puran Holdings, Dongxin Co., Beijing Junzheng, Lankai Technology, and storage module manufacturers Demingli, Shannon Semiconductor, Jiangbolong, etc.

🌟Performance in the past year

Since 2026, the storage sector has been one of the strongest branches in the AI industrial chain. In the U.S. stock market, driven by investment in AI infrastructure and high-capacity storage demand, Seagate, SanDisk, and Western Digital have all seen significant increases this year, with Reuters mentioning at the end of April that Seagate and Western Digital have doubled this year, with SanDisk increasing by approximately 350%. Storage chip manufacturers have also strengthened concurrently, with Micron seeing a substantial increase this year, while SK Hynix continues to benefit from HBM shortages and major manufacturers seizing production capacity, achieving a year-on-year revenue growth of 198% and an operating profit growth of 406% in the first quarter, further enhancing profitability.

In conclusion: First to rise is chips, next to supplement is electricity, and finally is storage

The first wave of realization for AI is chips; the second wave bottleneck is energy; the third wave of long-term benefits is storage.

The logic is correct, but it does not mean it is a comfortable buying point. There are structural opportunities, but it is not mindlessly chasing heights.

What is truly valuable is not the excitement itself, but which layer of the industrial chain you stand on.

Disclaimer: The above is merely a review of the industrial chain and does not constitute investment advice. Especially since some target stocks have had very exaggerated increases since 2026, correct logic does not equal comfortable buying points.