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LG Doucet and Milk Road’s AI analysts examine why rate fears, rising capital expenditure, Chinese open-weight models, and tougher guidance are pressuring AI infrastructure stocks. They argue the market is shifting from rewarding projected demand to demanding evidence that revenue and profits can materialize sooner.
The group explains how cheaper open models could broaden AI adoption while increasing demand for cloud infrastructure, GPUs, memory, and custom chips. They also debate hyperscaler spending, Google’s distribution advantage, Tesla’s path to monetizing autonomous driving, and whether AI applications and infrastructure can eventually rise together.
The discussion closes with a look at China’s memory industry, sovereign AI investment, and the earnings signals that could change market sentiment. The practical takeaway is a barbell view of the AI trade: exposure to both infrastructure and companies using AI to expand margins, while treating model providers and capital-intensive names more cautiously.
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