This is EXACTLY why I remain so bullish on the broader AI infrastructure buildout. Look at what is happening with GPU rental prices compared to AI token prices. Since July, the cost of AI tokens has collapsed, yet H100 GPU rental prices have remained elevated at roughly $2.70 per hour and this is a perfect example of Jevons Paradox. As AI becomes cheaper to use, developers and businesses can afford to use significantly more of it. Applications that were previously too expensive to run at scale suddenly become economically viable. Think about it, if the cost of running an AI agent drops significantly, companies can afford to deploy thousands of agents instead of hundreds. The same applies to coding assistants, AI search, customer support, and pretty much every application that relies on inference. And while each individual task might become cheaper, the total number of tasks being performed could grow significantly faster. That means more demand for GPUs, memory, networking equipment, data centers, and electricity and we're already seeing signs of this in the chart. Even as token prices have fallen sharply, H100 rental prices have remained relatively stable since July. That suggests demand for compute is still strong enough to support pricing despite the improvements we're seeing in AI efficiency. Now, there is an important distinction here. Cheaper tokens don't automatically guarantee higher GPU prices. Hardware supply, utilization rates, and newer GPU generations all play a role. But the fact that compute pricing has remained this resilient while token prices have declined tells us something important about underlying demand. I also think investors are underestimating just how much additional AI usage lower prices could unlock over the next several years. We're still relatively early in the adoption cycle, and many businesses have barely started integrating AI into their daily operations. This is why I continue to favor the companies supplying the infrastructure behind AI rather than trying to predict which model provider will ultimately dominate. Regardless of whether OpenAI, Anthropic, Google, or open source models capture the most market share, someone still needs to provide the compute required to run all of these models. And as long as AI usage continues growing faster than efficiency improvements reduce compute requirements, the broader infrastructure buildout should continue benefiting. Bullish on the broader AI infrastructure buildout. If you want to see how I’m investing across compute, memory, networking, and power, check out my full Milk Road Pro portfolio below: