$META $AMD $MU $SNDK $INTC $ARM Meta's Muse just took what OpenClaw proved an autonomous agent can do and removed the barrier to entry. The result: people who don't care about setting up technical stuff, or about AI itself, using a product that makes their lives easier. Ben Thompson called it "the best and most approachable personal agent product I have tried," and regular people seem to agree. No. 1 on the US App Store in ten days, 1.8M US and Canada iOS downloads in its first 12 days against 1.3M for ChatGPT, and 642K US daily users against 231K. THE MODEL IS CHEAP. THE COMPUTER ISN'T. On Cursor's own benchmark, Muse Spark 1.3 at max roughly matches GPT-5.6 Sol at max, 41.6% against 41.7%, at about a third of the cost per task. Meta didn't build the smartest model, it built one good enough and cheap enough to give away, and that moves the bill from the GPU to the machine the agent lives on. Every user gets their own always-on cloud machine, 2 vCPUs, 8GB of RAM and 100GB of SSD, which stays on whether you're using it or not. AGENTS DON'T CHURN LIKE CHATBOTS Switching chatbots takes a click. Once an agent holds your email, your calendar and your habits, leaving means starting over. Sticky users mean machines that stay switched on for a long time. And Meta plays this game from strength: it already runs the best personalization engine in consumer tech, and Adam Mosseri says it's only now getting as sophisticated as people always assumed it was. WHAT IT MEANS FOR THR AI TRADE At 100M users: 200M vCPUs, roughly 0.5M to 1.6M server CPUs depending on core counts and sharing, 800 PB of RAM and 10 EB of SSD. Meta will share hardware and park idle machines, so the real number lands lower, but every lab now has to answer with its own cloud computer per user. Chatbots made GPUs scarce.Agents add CPUs, DRAM and flash to the list. Bullish AF for the AI buildout ⚡