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BoFA: Weekly update on Memory > Stronger orders from $META: If Meta really is slowing down on AI build, the memo hasn't reached the purchasing team yet. Some chipmakers have categorically told META's orders are stronger than ever > Chinese tech inside $AAPL: Another market allegation which has no solid footing. This might be as a bargaining chip or a low impact move than a reality. Main reason: China restriction, Quality & Specs from CXMT and $DRAM IPs are owned by US/Korean players > Memory cycle not at its peak: Korean Govt. led investment spree doesn't mean memory peak. It would take at least 2033 for meaningful capacity to ease demand. Given strong enterprise / strong demand, peak isn't in yet. > Lower than expected numbers from Samsung: Bonus that was announced for their workers to avoid shut own will result in lower than bullish estimates of the results. > DDR4 has now inverted above DDR5: 16Gb DDR4 at $75.4 vs DDR5 at $46.8 (+61%). Suppliers exiting the legacy node faster than demand rolls off. DDR4 up 4 weeks straight, DDR5 stable, NAND flat WoW but still triple-digit YoY across every density. All in all - demand is broadening (HBM, LPDDR5, eSSD, SOCAMM), supply stays disciplined, and BofA sees shortage risk into 2027. The bear case on META capex and CXMT-in-AAPL. looks like noise. Watch Samsung's 2Q print (7 Jul): headline OP light on bonus + phone margin, but memory-only OP could beat on ASP. That's the number that matters. Repost for memory bears in your timeline to counter it to build an objective opinion. Are you positioned for a 2027 shortage, or still trading the 2026 peak that hasn't shown up? 👇

A paper on open model adoption and finds Chinese models, led by Qwen, now dominate. China passed the U.S. in open model downloads in summer 2025. By March 2026, Chinese models had 1.15B tracked downloads, while U.S. models had 723M. Most of that shift comes from Qwen, which became the default base family for many builders. Qwen’s lead is not just about 1 famous model; it comes from having many useful models across many sizes, especially small ones that people can run cheaply and reuse often. DeepSeek matters in a different way: it does not dominate the whole open model ecosystem like Qwen, but it leads the very large model category, especially models above 250B param After adjusting for model size and age, some U.S. models like GPT-OSS 120B and Nemotron Super 120B still show very strong adoption momentum, even though China leads overall. ---- Link – arxiv. org/abs/2604.07190 Title: "The ATOM Report: Measuring the Open Language Model Ecosystem"