AI Ate Your RAM — And There Was Never a Shortage

Your RAM got twice as expensive. Why? Not a fire. Not a shortage of factories — they're running flat out, record output. So where did your RAM go? Into AI. But not how you think. AI didn't buy your memory. The memory AI needs EATS the factory that makes yours. How much more silicon does a gigabyte of AI memory cost? About THREE times. Why so much? High-bandwidth memory is stacked twelve chips tall. To talk vertically, you drill thousands of holes straight through the silicon. Holes need keep-out space. Fewer bits per wafer. Ordinary DRAM is eighty-five percent denser. Worse? Stack twelve layers at ninety-nine percent yield each. Do you get ninety-nine? You get eighty-seven. Plus forty to sixty percent more process steps. So what happened to the world's memory? In 2024, AI memory took eight percent of DRAM wafers. This year, twenty-three. Total capacity? Up about two percent. And from the factory side, capacity was never chips. It's wafer starts. So is this a shortage? No. It's a reallocation. Nobody stopped making your RAM — the wafers it lived on are making something else. Now — your FabSpeak Tip of the Week. Same lesson, your desk. Last week DeepSeek shipped a small, cheap model that's genuinely good, while GPT-5.5 added a mode that spends MORE compute on hard problems. So are you routing, or defaulting? Paste your recurring AI tasks into any assistant and have it sort them — which truly need frontier reasoning across state that won't fit in one prompt, and which are just summarize, extract, classify, translate. I ran it on ten. Six were cheap-model work — and those six were the highest volume. Importance isn't reasoning depth. Save this, and go check where your compute budget actually goes. That's FabSpeak. One real thing from where AI meets the factory floor, every week. See you at the next one.