Articles

By Thomas Eugene Green

In February 2024, Raja Koduri put a clear prediction into the public record.

He said this. If the DRAM to SRAM cost ratio compresses far enough, chip architects will face forced design decisions. Once made, those decisions lock in for years due to long silicon cycles.

Most people scrolled past.

Ben Pouladian did not.

Ben followed the math. He tracked memory pricing, supply pressure, and design timelines. His Substack series, The Memory Cost Reckoning, shows why the memory wall no longer lives in theory. Memory economics now shape architecture, roadmaps, and competitive position through the end of the decade.

This matters because the signal is no longer subtle.

Here is what stood out to me from Ben’s work:

• The DRAM versus SRAM cost curve drives architecture choices.

• Latency and bandwidth now tie directly to dollars per token.

• Energy per token follows memory placement.

• Multi year silicon timelines turn pricing shifts into long term outcomes.

Raja did not speak in abstractions. He pointed to a measurable threshold and a clear consequence. Ben validated it with data. The conclusion is direct. As BeeRaja was right, and he called it early.

This reflects systems level thinking built over decades. Raja constantly weighs latency against bandwidth. Cost against performance. Energy against scale. He thinks about what happens when pricing signals collide with long silicon cycles.

At Oxmiq Labs, this mindset guides how we work every day. We are watching
memory economics reshape the entire AI stack. When memory hierarchies flip, architectures change, and competition resets.

The memory wall is no longer only about engineering limits. Cost and supply now drive the outcome. The real question is... who is designing for this reality? And it's worth remembering who saw it first. 😉