CoinWorld News, Cathay Pacific Haitong maintains an "Overweight" rating on the overseas AI computing power and application industry chain. Its research report points out that the super-fast growth of OpenAI and Anthropic, on one hand, verifies that the capability boundaries of AI models are continuously expanding, and on the other hand, drives the demand upward across the entire AI industry chain. It recommends focusing on three key areas: (1) hardware supply chains benefiting from the expansion of computing power by OpenAI and leading global model manufacturers, including GPU/ASIC, CPU, storage, advanced process and packaging, semiconductor equipment, etc.; (2) cloud providers and infrastructure platforms that handle AI workloads and model distribution; (3) application companies with full ecological and full-scenario implementation capabilities.

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RedGlass
· 4h ago
Cathay Haitong's research report is quite straightforward; when it comes to AI computing power, you really need to keep an eye on the hardware supply chain. GPUs and advanced packaging are now too critical.
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ForkMoment
· 5h ago
Among the three directions, I most want to see the application layer; whoever can achieve full-scenario implementation will be the next super app.
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0xPeachy
· 5h ago
The most difficult to invest in are full-ecosystem application companies, but once successful, they become ten-bagger stocks.
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UnderTheWisteriaBridge
· 5h ago
The ASIC route mentioned in the research report is worth paying attention to, opportunities beyond NVIDIA
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ForkItAnyway
· 5h ago
It is recommended to focus on semiconductor equipment, as there is significant potential for domestic substitution.
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FlamingoFacingJudgment
· 5h ago
Cloud providers handling AI workloads, this business is more stable than selling models.
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IceCreamUnderTheNeonLights
· 5h ago
Anthropic's growth confirms one thing: the big model competition is far from over, and the demand for computing power will only get crazier.
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Post-RainCancellationAgent
· 5h ago
Storage and advanced manufacturing processes are often overlooked; actually, the bottleneck lies here.
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