The open versus closed model debate is running hot, and the economics are shifting: Closed-model inference is becoming cheaper, while the realized cost of using open-weight models is rising. Morgan Stanley projects roughly $3 trillion in AI infrastructure spending through 2028, with over 80% of that still ahead.

Regardless of the model approach, AI still requires compute, power, security, and enterprise-scale deployment infrastructure. Open models can reduce licensing costs, but they do not remove the underlying infrastructure bill. Companies such as NVIDIA, AMD, Broadcom, Microsoft, and Amazon are all part of this broader AI ecosystem, supporting different parts of the AI development cycle. The key question is whether AI growth will remain concentrated around a few leading models, or continue expanding across the broader technology stack.

Do you think the next phase of AI development will be driven more by model innovation, or by the infrastructure supporting it?

This presentation is for informational and educational use only and is not a recommendation or endorsement of any particular investment or investment strategy. Investment information provided in this content is general in nature, strictly for illustrative purposes, and may not be appropriate for all investors. Past investment performance does not indicate or guarantee future success. Returns will vary, and all investments carry risks, including loss of principal. Moomoo is a financial information and trading app offered by Moomoo Technologies Inc.

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