Author: Changan I Biteye Content Team
Last November, Justin Sun posted a tweet:
If we treat this statement as an industry judgment, rather than a catchy catchphrase, we'll find in retrospect:
These three lines represent almost the most realistic profit path in AI-driven market trends.
If I had bought US storage stocks after that tweet came out, what would the result be today?
Micron: +214%
Seagate: +180%
• Western Digital: +190%
• SanDisk: +552%
This article will be analyzed along these three lines:
Why will AI first benefit the chip industry, then force out energy bottlenecks, and finally drive up storage demand in the long term? Which assets have already emerged as winners in this structural shift?
I. Chips: The first thing to materialize from the AI boom isn't narratives, but orders.
The AI industry first ignited at the underlying computing power level, not the application layer.
Whether it's training large models, or daily inference, agent invocation, or multimodal processing, the first step is to get the computation running, and all of this computation ultimately relies on GPUs, HBMs, high-speed interconnects, and advanced manufacturing processes.
In other words, the growth in demand for AI will not first be transmitted to later stages, but will first become a more direct reality:
We need more chips, more powerful chips, and chips with higher bandwidth.
This is why the demand for AI is first reflected in the chip sector.
Industry data has made this very clear. NVIDIA's revenue grew by 65% year-over-year in fiscal year 2026, indicating that demand for high-end computing chips continues to be strong.
🌟What assets are in this sector?
Core computing power layer: NVIDIA (NVDA), AMD, Broadcom (AVGO), TSMC (TSM)
Domestic computing power layer: Hygon Information ( 688041.SH ), Cambricon ( 688256.SH ), etc. Among them, Hygon Information is one of the leading x86 server CPU companies in China, with revenue of 9.162 billion yuan in 2024, a year-on-year increase of 52.4%.
Semiconductor equipment layer: ASML, Applied Materials (AMAT), and Lam Research (LRCX). ASML's ADR (American Depositary Receipt) price has already hit a record high at the start of 2026, with a single-day increase of over 8% on January 2nd, and a year-to-date increase of 27%; Lam Research's year-to-date increase is as high as 30%; and Applied Materials' year-to-date increase is as high as 28%. The stock prices of these three semiconductor equipment giants have all significantly outperformed the S&P 500 index.
🌟Performance over the past year
The semiconductor sector was the first to start and has seen the largest gains in this AI boom. Nvidia, as the leader, has seen a cumulative increase of over 1000% since the beginning of 2023. The device sector continued to reach new highs in early 2026, and the overall trend remains strong. A research report from Citigroup predicts that the global semiconductor equipment sector will enter a "Phase 2 bull market cycle," with ASML, Lam Research, and Applied Materials clearly leading the chip stock picks for 2026.
II. Energy: As AI scales up, the bottleneck shifts from chips to electricity.
No matter how many chips you have, it won't run without power.
Purchasing chips is just the beginning. The long-term operation of large models, data centers, and inference services requires continuous power supply and additional heat dissipation and cooling loads. Traditional data center rack power is typically 5 to 15 kilowatts, while AI data centers have significantly increased to 50 to 100 kilowatts—the power consumption and heat dissipation pressure are on completely different scales. An IEA analysis this year mentioned that data center electricity consumption will increase to approximately 945 TWh by 2030, roughly doubling from current levels, with AI being the primary driving force. The U.S. Department of Energy has also explicitly stated that the increasing power demand from data centers is putting significant pressure on regional power grids.
🌟What assets are in this sector?
Gas turbines: GE Vernova (GEV): Gas turbine orders are booming, with total orders reaching $59 billion in 2025 and backlog growing to $150 billion. Management has raised its 2026 revenue guidance to $44 billion to $45 billion.
Independent power producers: Constellation Energy (CEG): The largest zero-carbon power operator in the United States, with direct long-term power purchase agreements with tech giants for its nuclear power assets; Vistra (VST): Possesses both nuclear and gas power assets, with a median EBITDA guidance of approximately 30% higher than in 2025 for 2026.
Uranium Resources: Cameco (CCJ): The world's largest publicly traded uranium miner and an upstream beneficiary of the nuclear power restart.
🌟Performance over the past year
GE Vernova's stock price has risen 167% over the past year. Its 52-week low was $408, and it reached a high of $1181, nearly doubling in value. Constellation Energy hit an all-time high in 2025, before correcting by about 28% due to regulatory disruptions and is currently at a relatively low level. Vistra maintains its overall strength, with long-term power contracts for data centers continuing to be finalized. The energy sector as a whole has been repriced from a traditional defensive position to a core beneficiary of AI infrastructure.
III. Storage: The most easily overlooked area, but one that will benefit in the long run.
The core logic that benefits storage is simple: AI is not a one-time call; it is essentially a system that continuously processes, accumulates, and calls data.
Training requires reading a large amount of data, and checkpoints need to be stored during the training process. Inference requires adjusting the model and caching. RAG and Agent also need to continuously read the knowledge base, logs and memories.
In this way, AI brings not just "more data," but also:
• More frequent data read and write
• More real-time invocation
• More complex management
• Greater pressure on migration and caching
Looking further down the line, the more expensive the GPU, the less it can be idle. Therefore, the industry will pay more and more attention to how to send data to the computing power end faster and more stably.
In other words, the more AI develops, the less storage becomes just a "warehouse for storing data," but rather a data foundation that ensures the continuous operation of the entire AI system.
🌟What assets are in this sector?
Memory 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 companies include GigaDevice, ProLogium Technology, Dongxin Technology, Beijing Junzheng, and Montage Technology, as well as storage module manufacturers such as Demingli, Shannon Semiconductor, and Jiangbolong.
🌟Performance over the past year
Since 2026, the storage sector has been one of the strongest branches in the AI industry chain. In the US stock market, driven by investment in AI infrastructure and demand for high-capacity storage, Seagate, SanDisk, and Western Digital have all seen significant gains this year. Reuters reported in late April that Seagate and Western Digital have more than doubled this year, while SanDisk has risen by approximately 350%. Storage chip manufacturers have also strengthened, with Micron experiencing a substantial increase this year, while SK Hynix continues to benefit from HBM shortages and competition for capacity among major manufacturers, reporting a 198% year-on-year increase in revenue and a 406% year-on-year increase in operating profit in the first quarter, further strengthening its profitability.
In conclusion: Chips saw the price surge first, followed by electricity, and finally storage.
The first wave of AI's realization came from chips; the second bottleneck was energy; and the third, long-term beneficiary is storage.
Correct logic doesn't equate to a comfortable entry point. Structural opportunities exist, but that doesn't mean blindly chasing high prices.
What's truly valuable isn't the excitement itself, but rather where you stand in the industry chain.
Disclaimer: The above is merely a review of the industry chain and does not constitute investment advice. In particular, some stocks have seen extremely significant price increases since 2026; sound logic does not equate to a comfortable entry point.
