Chips, Energy, Storage—the Three Pillars of AI Infrastructure: Which Will Rise First, Surge Most, and Still Have Room to Catch Up?
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Chips, Energy, Storage—the Three Pillars of AI Infrastructure: Which Will Rise First, Surge Most, and Still Have Room to Catch Up?
First up are chips, followed by power electronics, and finally memory.
Navigating Web3 tides with focused insightsAuthor: Changan I Biteye Content Team
In November last year, Justin Sun posted a tweet:
If we treat this statement as an industrial insight—not just a viral soundbite—looking back reveals that:
These three trends represent the most authentic return pathways of the AI bull market.
What would have happened if you had bought U.S.-listed memory-related stocks right after that tweet?
• Micron: +214%
• Seagate: +180%
• Western Digital: +190%
• SanDisk: +552%
This article unpacks those three trends:
Why does AI first benefit chips, then expose energy bottlenecks, and ultimately drive long-term storage demand? Which assets have already pulled ahead in this structural shift?
I. Chips: The First Realized Benefit of AI Is Not Narrative—It’s Orders
What ignites first in AI is not the application layer—but foundational compute power.
Whether training large models, performing daily inference, invoking agents, or handling multimodal tasks, the first step is always to execute computation—and all such computation ultimately rests on GPUs, HBM, high-speed interconnects, and advanced process nodes.
In other words, rising AI demand doesn’t trickle down gradually through the value chain. Instead, it immediately manifests as tangible reality:
More chips. Better chips. Chips with higher bandwidth.
That’s why AI demand first shows up in the chip sector.
Industry data makes this crystal clear. On a fiscal 2026 basis, NVIDIA’s revenue grew 65% year-on-year—indicating sustained demand for high-end compute chips.
🌟Key Assets in This Segment
Core Compute Layer: NVIDIA (NVDA), AMD, Broadcom (AVGO), TSMC (TSM)
Domestic Compute Layer: Hygon Information (688041.SH), Cambricon (688256.SH). Hygon is one of China’s leading x86 server CPU vendors; its 2024 revenue reached RMB 9.162 billion, up 52.4% year-on-year.
Semiconductor Equipment Layer: ASML, Applied Materials (AMAT), Lam Research (LRCX). ASML’s U.S. ADR hit an all-time high at the start of 2026—rising over 8% on January 2 alone, and up 27% year-to-date. Lam Research gained 30% YTD; Applied Materials rose 28% YTD. All three major semiconductor equipment giants significantly outperformed the S&P 500 Index.
🌟Performance Over the Past Year
The chip sector was the earliest and strongest-performing theme in this AI bull run. As the industry leader, NVIDIA has surged over 1000% since early 2023. Equipment stocks continued hitting new highs in early 2026 and remain firmly in an upward trend. Citigroup recently published a report forecasting a “Phase 2 bull market” for the global semiconductor equipment sector, identifying ASML, Lam Research, and Applied Materials as the clear 2026 chip stock leaders.
II. Energy: Once AI Scales, Bottlenecks Shift from Chips to Power
No matter how many chips you buy—you still can’t run them without electricity.
Purchasing chips is only the beginning. Running large models, data centers, and inference services sustainably requires continuous power supply—and incurs additional thermal management and cooling loads. Traditional data center racks typically consume 5–15 kW per rack, while AI data centers now operate at 50–100 kW per rack—representing an entirely different magnitude of power consumption and thermal stress. According to the IEA’s analysis this year, global data center electricity demand will rise to approximately 945 TWh by 2030—nearly doubling current levels—with AI as the primary driver. The U.S. Department of Energy has also explicitly stated that surging data center power demand is placing visible strain on regional grids.
🌟Key Assets in This Segment
Gas Turbines: GE Vernova (GEV): Gas turbine orders are surging—full-year 2025 orders reached $59 billion, with backlog climbing to $150 billion. Management raised its 2026 revenue guidance to $44–45 billion.
Independent Power Producers: Constellation Energy (CEG): The largest zero-carbon power operator in the U.S., with nuclear assets under long-term power purchase agreements directly signed with tech giants; Vistra (VST): Combines nuclear and gas-fired generation assets—its 2026 EBITDA guidance midpoint reflects ~30% growth over 2025.
Uranium Resources: Cameco (CCJ): The world’s largest publicly traded uranium producer—and a key upstream beneficiary of nuclear power’s resurgence.
🌟Performance Over the Past Year
GE Vernova’s share price rose 167% over the past year—trading between a 52-week low of $408 and a high of $1,181, nearly doubling in range. Constellation Energy hit an all-time high in 2025 but retreated ~28% from that peak due to regulatory headwinds, leaving it currently at relatively attractive levels. Vistra has maintained strength, with long-term power supply contracts for data centers continuing to close. Overall, the energy sector has been revalued—not as a traditional defensive holding, but as a core beneficiary of AI infrastructure.
III. Storage: The Most Overlooked—Yet Longest-Term—Beneficiary
The core logic behind storage’s upside is straightforward: AI isn’t about one-off queries—it’s a system built on continuous data ingestion, persistent data accumulation, and repeated data retrieval.
Training requires reading massive datasets; checkpoints must be saved during training; inference calls models and relies on caching; RAG and agent architectures constantly access knowledge bases, logs, and memory.
This means AI drives more than just “more data”—it drives:
• More frequent data reads and writes
• More real-time data access
• More complex data management
• Greater pressure on data migration and caching
Going further: As GPUs become increasingly expensive, idle time becomes unacceptable—so the industry places ever-greater emphasis on delivering data faster and more reliably to the compute layer.
In other words, the more AI advances, the less storage functions merely as a “data warehouse”—and the more it evolves into the foundational data infrastructure ensuring seamless, continuous AI system operation.
🌟Key Assets in This Segment
Memory Chip Manufacturers: SK Hynix (000660.KS), Samsung Electronics (005930.KS), Micron Technology (MU)
NAND / SSD / HDD Vendors: SanDisk (SNDK), Seagate (STX), Western Digital (WDC)
Domestic Memory Designers: GigaDevice, Puyuan Semiconductor, Dongxi Semiconductor, Beijing Junzheng, Montage Technology; memory module makers include Demingli, Shannon Core, and Jiangbolong.
🌟Performance Over the Past Year
Since the start of 2026, the storage segment has been among the strongest in the AI supply chain. In U.S. markets, driven by AI infrastructure investment and demand for high-capacity storage, Seagate, SanDisk, and Western Digital have all posted substantial gains—Reuters noted at end-April that Seagate and Western Digital had more than doubled year-to-date, while SanDisk rose ~350%. Memory chip manufacturers have also strengthened: Micron has surged sharply this year, while SK Hynix continues benefiting from HBM shortages and aggressive capacity grabs by major customers—posting 198% YoY revenue growth and 406% YoY operating profit growth in Q1, further strengthening profitability.
Final Thoughts: Chips Rise First, Power Catches Up Next, Storage Follows Last
The first wave of AI realization hits chips; the second wave exposes energy bottlenecks; the third—and longest-lasting—wave benefits storage.
Sound logic doesn’t guarantee comfortable entry points. Structural opportunities exist—but blind chasing of highs is unwise.
What truly matters isn’t the hype itself—but where you sit within the value chain.
Disclaimer: The above is a retrospective analysis of the industry chain only and does not constitute investment advice. Notably, some securities have posted extraordinary gains since the start of 2026—sound logic does not equal favorable timing.
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