This graph, showing the explosion in agent-based AI usage, speaks volumes. According to OpenRouter data, agent token usage has surpassed human usage, increasing 14-fold in a very short time to reach 7.3 trillion.
Why is this important?
Because when thousands of agents are running simultaneously, it's not just about more computing power; it's about more memory. Especially with long context, inference, and concurrent workloads, memory capacity and bandwidth become one of the most critical bottlenecks. This is because agents increasingly need memory to remember their assigned tasks, past work, etc.
This is exactly what Micron explains in the post I quoted. The foundation of next-generation AI infrastructure is not just GPUs, but memory.
Therefore, we shouldn't still view $MU as an old-fashioned "cyclic memory share." Micron is no longer a company solely focused on PCs and phones. Those days are over. It's in the midst of a much more structural transformation thanks to HBM, DRAM, SOCAM, and data center-focused memory demand.
As AI agents grow, memory will become critical. As memory becomes more critical, Micron's pricing power will increase. As pricing power increases, the "cyclical" label will weaken considerably.
In short, in the age of AI, bots cannot function without memory. I believe Micron will greatly benefit from this. $MU Micron says: Agentic AI doesn't just increase demand on the GPU side in data center architecture; it structurally increases the need for CPU and especially DRAM.
According to Micron, in classic chatbot-type AI, the majority of the workload was model inference on the GPU. In agent systems, however, much more CPU-intensive tasks are added around this, such as planning, tool usage, API calls, code execution, database queries, retrieval, and scheduling.
Miron explains this with a nice analogy: If the GPU is the factory floor, the CPU is the management office. This change could push the CPU/GPU ratio from the historically approximately 1:4 level to 1:1 or 1:2. Micron argues that this could mean a 2-4 fold increase in CPU demand. This is quite positive for AMD, INTC, ARM, and TSM.
The real strength lies in the memory side. Each running AI agent needs DRAM for state, context/KV data, tool outputs, queues, sandbox/container memory, vector indexes, and runtime overhead. When thousands of agents start running concurrently in a single rack, this memory requirement grows rapidly. Micron also states that in some agentic workloads, up to 90% of latency can be due to CPU-side tool processing, therefore memory capacity and bandwidth can directly become a performance bottleneck.
The most important investment takeaway is this: Micron suggests that DRAM demand can grow not linearly, but with a multiplier effect. For example, if the number of agents/containers doubles while the memory requirement per agent also doubles, the total DRAM requirement quadruples. If both triple, the theoretical requirement increases ninefold. Micron calls this the "second scaling curve" of agentic AI.
The logic behind this is important. As the number of agents increases, the memory footprint per agent is not expected to remain constant. More complex tasks, longer contexts, more tool usage, and more parallel environments consume more memory. So, two separate curves are simultaneously moving upwards:
Number of agents ↑
× memory per agent ↑
= much faster growth in total DRAM demand.
This makes the OpenRouter graph I shared earlier more meaningful. If Agentic token usage continues to grow at this rate (which I believe it will because we are still in the very beginning), the beneficiaries will not only be accelerator manufacturers like Nvidia ($NVDA). CPU, DRAM, and memory bandwidth providers will also become direct, not secondary, infrastructure beneficiaries.
For Micron ($MU), I think the most critical message of this article is this: the company is telling investors, "Don't judge us solely by the HBM story." Agentic AI can also increase the demand for high-capacity, high-bandwidth classic DRAM. Micron clearly envisions the future of rack architecture shifting from a GPU-dominant structure to one with a more balanced CPU, GPU, and DRAM configuration.
In short, the first phase of AI, the chatbot phase, created a GPU shortage; the second phase of agentic AI could simultaneously create a CPU and memory shortage. Micron positions itself precisely at the center of this second wave.
