AI models may change leaders quickly, but every serious competitor still needs compute, memory, networking, cooling and electricity. That makes the infrastructure layer particularly interesting because it can benefit regardless of whether OpenAI, Meta, Anthropic or another player ultimately wins the model race.
I’m also watching D closely. The scale of AI capex is becoming enormous, so eventually revenue and free cash flow must justify it. Spending hundreds of billions is bullish for infrastructure suppliers, but not necessarily for the companies writing the cheques.
My preferred approach is therefore to follow the bottlenecks: GPUs → HBM → networking → cooling → power. As one constraint gets solved, capital tends to move towards the next.
The AI opportunity still looks huge, but the next winners may be those selling the scarce infrastructure rather than the most exciting chatbot. 📊
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