When we talk about the cost of AI infrastructure, the focus is usually on Nvidia and GPUs — but memory is an increasingly important part of the picture. As hyperscalers prepare to build out billions of dollars worth of new data centers, the price for DRAM chips has jumped roughly 7x in the last year.
At the same time, there’s a growing discipline in orchestrating all that memory to make sure the right data gets to the right agent at the right time. The companies that master it will be able to make the same queries with fewer tokens, which can be the difference between folding and staying in business.
Semiconductor analyst Dan O’Laughlin has an interesting look at the importance of memory chips on his Substack, where he talks with Val Bercovici, chief AI officer at Weka. They’re both semiconductor guys, so the focus is more on the chips than the broader architecture; the implications for AI software are pretty significant too.
I was particularly struck by this passage, in which Bercovici looks at the growing complexity of Anthropic’s prompt-caching documentation:
The tell is if we go to Anthropic’s prompt caching pricing page. It started off as a very simple page six or seven months ago, especially as Claude Code was launching — just “use caching, it’s cheaper.” Now it’s an encyclopedia of advice on exactly how many cache writes to pre-buy. You’ve got 5-minute tiers, which are very common across the industry, or 1-hour tiers — and nothing above. That’s a really important tell. Then of course you’ve got all sorts of arbitrage opportunities around the pricing for cache reads based on how many cache writes you’ve pre-purchased.
The question here is how long Claude holds your prompt in cached memory: you can pay for a 5-minute window, or pay more for an hour-long window. It’s much cheaper to draw on data that’s still in the cache, so if you manage it right, you can save an awful lot. There is a catch though: every new bit of …