Bitmine Chairman and Fundstrat co-founder Tom Lee said relative performance in what he calls "AI downstream" assets continues to strengthen, with Ethereum standing out against the DRAM segment. According to data cited by Lee, ETH’s performance advantage over DRAM widened to 7,200 basis points, or 72 percentage points, over the past month. During the same period, ETH rose about 24%, while a DRAM-related ETF fell about 38%.
Lee said the shift reflects how AI infrastructure spending is moving beyond chips and compute supply chains toward the application layer and digital infrastructure. In that context, some downstream AI-linked assets are being repriced by the market. A long-time Ethereum bull, Lee has repeatedly argued that the network could become a core piece of digital economic infrastructure in the AI era as stablecoins, tokenized assets, and on-chain financial applications expand. The latest comparison, he said, points to a widening performance gap between crypto assets and the traditional AI hardware supply chain.
Tom Lee, chairman of Bitmine and co-founder of Fundstrat, said relative performance in "AI downstream" assets is continuing to improve, with Ethereum posting clear excess returns against the DRAM segment in recent weeks.
Data shared by Lee showed that over the past month, ETH’s performance advantage over DRAM widened to 7,200 basis points, or 72 percentage points. Over the same stretch, ETH gained about 24%, while a DRAM-related ETF fell about 38%.
Lee said that as AI infrastructure investment spreads from chips and compute supply chains toward the application layer and digital infrastructure, some "AI downstream" assets are being repriced by the market.
Lee has long taken a constructive view on the Ethereum ecosystem. He has previously said that as stablecoins, tokenized assets, and on-chain financial applications develop, Ethereum could become an important part of the digital economic infrastructure of the AI era.
The comparison also shows a widening divergence in performance between crypto assets and the traditional AI hardware supply chain.
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