Chips, energy, and storage: Which of the three AI infrastructure sectors will rise first, which will surge the most, and which can still be chased?

Using Justin Sun's tweet as a hook, the article highlights how the AI sector's profits follow a "chips → energy → storage" sequence, with storage being the most overlooked yet delivering staggering gains.

Chips: AI first boosts computing power, with NVIDIA surging over 1000% since early 2023 and equipment giants like ASML up 27% in early 2026.
Energy: AI data centers create power bottlenecks; electricity demand is projected to double by 2030, benefiting gas turbine leader GE Vernova (up 167% YoY) and nuclear operators.
Storage: AI's constant read/write needs drive storage demand; US storage stocks like SanDisk jumped ~350% YTD, Seagate and Western Digital doubled, while SK Hynix saw revenue up 198% YoY on HBM shortages.
The industrial logic is clear, but some stocks have seen exaggerated gains—correct logic doesn't always mean comfortable entry points.

Author: Changan I Biteye Content Team

Last November, Justin Sun posted a tweet:

Short-term chip shortages, long-term energy shortages, and a perpetual storage shortage—the future of BitTorrent is unimaginable.

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 chip 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 overall remains in a strong upward cycle.

A research report released by 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 in 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 the chips is just the beginning. To truly run large models, data centers, and inference services for extended periods, continuous power supply is required, along with additional heat dissipation and cooling loads.

Traditional data center rack power is typically between 5 and 15 kilowatts, while AI data centers have significantly increased to 50 to 100 kilowatts, with power consumption and heat dissipation pressure on a completely different scale.

This year's IEA analysis indicates that data center electricity consumption will increase to approximately 945 TWh by 2030, roughly doubling from current levels, with AI being the primary driver. The U.S. Department of Energy has also explicitly stated that the growth in data center power demand 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 producer: Constellation Energy (CEG): The largest zero-carbon power operator in the United States, with nuclear power assets directly signed long-term power purchase agreements with technology giants;

Vistra (VST): Possesses both nuclear power and gas assets; its 2026 EBITDA guidance median is expected to increase by approximately 30% compared to 2025.

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. The 52-week low was $408 and the high was $1,181, representing a nearly 200% increase.

Constellation Energy reached an all-time high in 2025, but subsequently retreated by about 28% from its peak due to regulatory policy disruptions, and is currently at a relatively low level.

Vistra maintained its strong performance overall, 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 at the end of April that Seagate and Western Digital have more than doubled this year, while SanDisk has risen by about 350%.

Memory chip manufacturers also strengthened, with Micron rising sharply this year, while SK Hynix continued to benefit from the shortage of HBM and the competition for production capacity among major manufacturers. Its first-quarter revenue increased by 198% year-on-year, and its operating profit increased by 406% year-on-year, 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.