Original Author: Changan I Biteye Content Team

In November of last year, Justin Sun posted a tweet:

If we treat this statement as an industry judgment rather than a viral quote, looking back reveals:

These three narratives represent nearly the most realistic profit path of the AI market trend.

If you had bought US stock storage concept stocks after that tweet, what would the results be today?

• Micron: +214%

• Seagate: +180%

• Western Digital: +190%

• Sandisk: +552%

This article breaks down these three narratives:

Why does AI first benefit chips, then squeeze energy bottlenecks, and finally drive long-term storage demand? Which assets have already emerged in this structural trend?

I. Chips: The First to Realize AI's Boom – Not Narratives, But Orders

What AI ignites first is not the application layer, but underlying computing power.

Whether it's training large models, daily inference, Agent invocations, or multimodal processing, the first step is to get the computing running. All of this ultimately relies on GPUs, HBM, high-speed interconnects, and advanced manufacturing processes.

In other words, the growth in AI demand doesn't trickle down to very downstream sectors first; it materializes as a more immediate reality:

More chips needed, stronger chips needed, higher bandwidth chips needed.

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

Industry data has made this very clear. Based on the FY2026 outlook, NVIDIA's revenue grew 65% year-over-year, indicating that demand for high-end computing chips continues to be released.

🌟Assets in this direction

Core Computing Layer: NVIDIA (NVDA), AMD, Broadcom (AVGO), TSMC (TSM)

Domestic Computing Layer: Haiguang Information (688041.SH), Cambricon (688256.SH), etc. Haiguang Information is one of the representative domestic x86 server CPU companies, with 2024 revenue of 9.162 billion RMB, up 52.4% year-over-year.

Semiconductor Equipment Layer: ASML, Applied Materials (AMAT), Lam Research (LRCX). ASML's US ADR price hit an all-time high at the start of 2026, surging over 8% on January 2nd alone, with a year-to-date gain of 27% in 2026. Lam Research is up 30% year-to-date, and Applied Materials is up 28%, significantly outpacing the S&P 500 index.

🌟Performance over the past year

The chip sector is the earliest and most significantly appreciated direction in this wave of AI market trends. As the leader, NVIDIA has accumulated gains of over 1000% since the beginning of 2023. Equipment companies continued to hit new highs in early 2026, remaining in a strong upward cycle. Citigroup released a report predicting a "Phase 2 bull market upcycle" for the global semiconductor equipment sector, identifying ASML, Lam Research, and Applied Materials as the clear mainstays for chip stocks in 2026.

II. Energy: As AI Scales Up, the Bottleneck Shifts from Chips to Electricity

No matter how many chips you have, they can't run without power.

Buying chips is just the beginning. The long-term operation of large models, data centers, and inference services requires a continuous power supply, along with additional loads for cooling and heat dissipation. Traditional data center racks typically consume 5 to 15 kW, while AI data centers have significantly raised this to 50 to 100 kW, placing entirely different magnitudes of pressure on power consumption and cooling. The IEA's analysis this year mentions that data center electricity consumption will increase to approximately 945 TWh by 2030, roughly doubling current levels, with AI being the primary driver. The US Department of Energy has also explicitly stated that the growing electricity demand from data centers is putting significant pressure on regional power grids.

🌟Assets in this direction

Gas Turbines: GE Vernova (GEV): Orders for gas turbines are booming, with total 2025 orders reaching $59 billion and backlog orders growing to $150 billion. Management has raised its 2026 revenue guidance to between $44 billion and $45 billion.

Independent Power Producers: Constellation Energy (CEG): The largest zero-carbon power operator in the US, with nuclear assets directly signing long-term power purchase agreements with tech giants. Vistra (VST): Has both nuclear and gas assets, with its mid-point 2026 EBITDA guidance approximately 30% higher than 2025.

Uranium Resources: Cameco (CCJ): The world's largest publicly traded uranium miner, a key upstream beneficiary of the nuclear power renaissance.

🌟Performance over the past year

GE Vernova's stock price has risen 167% over the past year. Its 52-week low was $408, reaching a high of $1,181, a near-doubling in the range. Constellation Energy hit an all-time high in 2025 but subsequently corrected about 28% from its peak due to regulatory policy uncertainty, currently trading at a relative low. Vistra has maintained its strength overall, with long-term power supply contracts with 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 Overlooked, But Long-Term Beneficiary

The core logic favoring storage is simple: AI is not a one-time call; it's fundamentally a system for continuous throughput, continuous data sedimentation, and continuous data recall.

Training requires reading vast amounts of data, saving checkpoints during the process. Inference requires loading models and caches. RAG and Agents constantly need to access knowledge bases, logs, and memories.

Consequently, AI doesn't just mean "more data"; it means:

• More frequent data reads and writes

• More real-time data retrieval

• More complex data management

• Greater pressure on data migration and caching

Looking deeper, the more expensive the GPUs get, the less they can afford to be idle. So the industry will increasingly focus on how to deliver data faster and more stably to the computing end.

In other words, the more AI develops, storage is no longer just a "warehouse for data," but the data infrastructure that ensures the entire AI system can operate continuously.

🌟Assets in this direction

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 Memory Design: Gigadevice, Puya Semiconductor, Dosin Semiconductor, Beijing Ingenic, Montage Technology, and storage module makers like Netac Technology, Shenzhen Transsion Trading (Xiangnong Xinchuang), and Longsys Electronics, etc.

🌟Performance over the past year

Since the beginning of 2026, the storage sector has been one of the strongest segments within the AI industry chain. Driven by AI infrastructure investment and high-capacity storage demand, Seagate, Sandisk, and Western Digital have all surged significantly year-to-date, as reported by Reuters in late April, with Seagate and Western Digital more than doubling, and Sandisk rising about 350%. Memory chip manufacturers have also strengthened simultaneously. Micron has risen sharply this year, while SK Hynix continues to benefit from HBM shortages and capacity grabs by major manufacturers, reporting Q1 revenue up 198% year-over-year and operating profit up 406%, further strengthening its profitability.

Final Thoughts: First to Rise are Chips, Next to Fill are Energy, Finally is Storage

The first wave of AI monetization is chips; the second bottleneck is energy; the third wave of long-term beneficiaries is storage.

Correct logic doesn't mean comfortable entry points. Structural opportunities exist, but it's not about blindly chasing highs.

What truly holds value is not the hype itself, but which layer of the industry chain you are positioned on.

Disclaimer: The above is merely a review of the industry chain and does not constitute investment advice. Especially given that some targets have already experienced very significant gains since 2026, correct logic does not equate to a comfortable entry point.