KLA gains as more complex AI chips require more inspection before they ship. 3D stacking, advanced packaging and 2nm processes multiply checks in a market KLA says it owns almost entirely. Revenue rose 29% and EPS 39%; the next-quarter revenue guide of $4.7bn exceeds the $4.54bn expected, showing the promised second-half acceleration. Overweight, $300 target. x.com  x.com 

Next-generation AI systems could double wafer demand, keeping the long-term outlook compelling for semiconductor equipment makers. Applied Materials targets more than $12 billion in services revenue and roughly 40% operating margins by 2030; management expects over two points of share gains and potentially more than $5 billion in advanced-packaging revenue, driven by HBM and AI demand. Bullish on Applied Materials, Lam Research, ASML and KLA. x.com 

Inference's move toward edge devices and dense, memory-hungry racks should drive demand for specialized chips, links, power delivery and cooling. McKinsey projects inference at 60% of AI computing demand by 2030. Ambarella makes on-device AI chips; Astera Labs links GPUs, memory and networks; Credo supplies short copper connections; Rambus earns from memory interfaces; Navitas reduces power waste; Modine cools dense racks; and Super Micro builds inference-specific server racks at volume. x.com  … Show more

US semiconductor equipment makers are the sturdiest picks-and-shovels play, because any industry expansion needs equipment first, giving them a place in every bull market. In the latest US tech selloff, chip and optical stocks fell broadly, yet equipment names jumped double digits at the open and nearly all closed higher, a clear vote by investors. The group spans Applied Materials, Lam, KLA, MKS, Entegris, Onto, Nova and ACM Research, with Axcelis, Ultra Clean and Veeco strongest. x.com 

IBM's sub-1-nanometer nanostack chip cements a multi-year capex cycle for lithography and process equipment leaders ASML, Lam Research and Tokyo Electron, which must power the move into the angstrom era. It tackles the two biggest bottlenecks for next-generation AI infrastructure: IBM says it delivers 70% better energy efficiency, vital to cutting data center operating costs, and its 40% denser SRAM is a big leap for memory bandwidth in heavy LLMs. x.com 

ASML, TSMC and BE Semiconductor are the safer, easier-to-understand way to own AI because they sit in the industrial part of the chain. The big question in AI is timing, which no one can call, and Nvidia's software moat is impossible to understand or trust with confidence. A quoted post notes that Crésus sold Nvidia, expecting faster AI progress to favor the owners of models and factories over chip designers. x.com 

ASML is the gatekeeper of the semiconductor industry as the only company that makes the machines that make the most advanced chips, so every AI boom, chip war and fab expansion becomes an ASML order. Nvidia, a $5 trillion company, could not exist without it, and the dependency runs only one way. Its 38.8B backlog booked into 2027, highlighted by @kunkelcapital, already holds the receipts for the next two years. x.com  x.com  x.com  x.com  x.com 

Aehr Test Systems is growing fast across several verticals, with its lead AI processor customer placing another $22M follow-on order for wafer-level burn-in systems. The tiny company is expanding into memory and silicon photonics as well, and at about $1.1B market cap it looks extremely promising, resembling an early Teradyne. x.com 

Intel getting High-NA EUV working secures ASML's technological moat for the next decade, mattering more than the raised guidance that moved the stock. ASML confirmed High-NA has entered commercial production with lead customer Intel, removing the biggest technical overhang on whether its $350M+ machines work in high-volume manufacturing. Q2 net sales of €9.3 billion and EPS of €7.59 beat estimates with a 54% gross margin, and FY2026 revenue guidance rose to €43-45 billion from €34-39 billion. … Show more

AI memory demand is expanding beyond HBM as inference increasingly needs capacity as well as bandwidth, keeping memory one of AI's most important infrastructure themes. SK hynix and Micron lead HBM, SK hynix and SanDisk are positioned in emerging high-bandwidth flash and NAND, Rambus supplies memory IP, TSMC, Amkor and ASE handle packaging, and Onto and Camtek supply inspection. x.com