The strongest semiconductor investment list in the AI era:

1. GPU (Graphics Processing Unit)
NVIDIA $NVDA
Almost monopolizes the high-end AI training GPU market; behind ChatGPT, autonomous driving, robots, and large model training, NVIDIA's computing power is indispensable.
AMD $AMD
One of the strongest challengers in AI GPUs, rapidly gaining market share.
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2. CPU (Central Processing Unit)
AMD $AMD
Continues to expand in the server CPU market, benefiting significantly from the explosive demand for AI servers.
Intel $INTC
The traditional CPU leader, with a large customer base and advantages in U.S. domestic manufacturing.
NVIDIA $NVDA
Is laying out an AI CPU ecosystem, with the future goal of creating a complete AI data center platform.
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3. TPU (Tensor Processing Unit)
Broadcom $AVGO
One of the core beneficiaries of AI data centers, also controls high-speed network chips and AI accelerators.
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4. HBM (High Bandwidth Memory)
Micron $MU
One of the most critical core components for AI servers; without HBM, large model training efficiency would drop significantly.
SK Hynix $TSM
Currently one of the global leaders in HBM, almost capturing a large portion of NVIDIA's HBM orders.
Samsung Electronics $INTC A global memory giant, also fully competing for the HBM market, an important player in future AI memory competition.
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5. Foundry (Wafer Manufacturing)
TSMC $MU
The world's most core advanced process foundry.
Intel $MU
Is vigorously developing its wafer foundry business, supported by U.S. chip policies.
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6. RAM (Memory)
Micron $MU
As AI models grow larger, demand for RAM increases; an important infrastructure company in the AI era.
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7. NAND Flash Memory Chips
Micron $SNDK
Driven by AI and cloud computing, data volume surges, long-term benefits for NAND demand.
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8. SSD (Solid State Drive)
Micron $WDC
AI servers and data centers require大量高速SSD存储。
SanDisk An important brand for SSDs and flash memory, benefiting long-term from AI data growth.
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9. HDD (Hard Disk Drive)
Western Digital In the AI era, not only training but also long-term storage of massive data is needed; HDD remains an important low-cost solution for large data centers.
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In the future, do you think the biggest bottleneck for AI will be GPU computing power or the increasingly scarce HBM high-bandwidth memory?