""As inference rises, so will memory and storage demand," Bleeker explains. "What you have to do is you have to hold the model weights in high bandwidth memory, which creates that incredible need for high bandwidth memory." High-bandwidth memory has become critical because AI model weights must reside close to compute, making memory a dominant cost and potential bottleneck in AI systems."
""One of the things that's happening with AI is that commodities are suddenly becoming precious. Memory was always just a commodity in tech. Same with copper, energy - now they're all becoming precious inputs." The AI Investor Podcast Is Your Number Source for AI News This conversation was from our most recent episode of The AI Investor Podcast. It's free to subscribe in your favorite podcast player and every episode we give updates and recommendations in a $500,000 portfolio filled with our top AI stock ideas."
Inference growth, larger context windows, and reasoning models are dramatically increasing demand for memory and storage. Model weights must be held in high-bandwidth memory close to compute, turning memory into a dominant cost and potential bottleneck in AI systems. Rising memory needs are prompting greater investment in semiconductor equipment and manufacturing capacity to alleviate supply constraints. Commodities previously treated as inexpensive inputs, such as memory, copper, and energy, are becoming scarce and precious for AI infrastructure. Investors should monitor trends in high-bandwidth memory, storage scaling, and equipment spending as key drivers of AI sector returns.
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