Everyone is chasing $SIVE or looking for the next $AEHR or $AXTI. I think I found it… Not one. But two. Both sitting at the exact chokepoint. This is maybe my favourite trade ideas the market hasn’t priced yet: CONSTRAINT 1: HBM inspection AI chips are not a single piece of silicon. A modern $NVDA GPU is a stack. A logic die at the bottom. Four to eight HBM memory dies bonded on top. Each memory die connected to the next through thousands of through-silicon vias, copper pillars drilled through the chip itself. Then that entire memory stack gets attached to the logic die through thousands more micro-pillar interconnects. Each pillar is smaller than a human hair. One defective pillar. One. That’s all it takes to kill a $40,000 AI GPU package. No buffer. No workaround. The whole unit is scrap. And here’s the constraint that makes this critical right now: HBM supply is sold out through at least 2027. No significant new capacity comes online until late 2027. There is no spare capacity. Every die that gets made needs to reach a GPU. A defect found late in the process isn’t a minor setback, it’s a $40,000 unit written off with nothing to replace it. So the industry doesn’t sample-inspect HBM stacks. It performs 100% INSPECTION. Every device. Every pillar. Every generation. As HBM advances from HBM3e to HBM4, the die gets larger, the micro-pillar density increases, and the inspection requirement becomes more complex, not less. There is one company with qualified equipment for this job at a leading US memory manufacturer. $COHU - Cohu Inc. Their Neon platform performs full 6-sided optical inspection of every HBM device using proprietary AI-trained software, defect recognition trained specifically on each customer’s device architecture. You can’t buy a competitor’s system and retrain it in a quarter. The switching cost is measured in years. The numbers: → $488M cash. No dilution risk. → Orders up 163% year-over-year Q1 2026 → $750M pipeline. 5 customers in active qualification. → HBM revenue guidance raised from $15M to $80–100M in a single year The market is pricing this as a test equipment cycle recovery. The correct frame: the only qualified inspection bottleneck in the HBM supply chain. Test equipment multiple: 3–4x EV/Revenue. AI infrastructure bottleneck multiple: 7–10x. CONSTRAINT 2: Silicon photonics fabrication Copper wires are hitting their physical limit inside AI data centers. Moving data between GPUs at the speeds AI training requires generates heat, signal loss, and power draw that copper interconnects can no longer handle efficiently. The industry’s answer is silicon photonics, lasers built directly onto chips, transmitting data as light instead of electrons. Co-packaged optics (CPO) embeds those lasers directly into AI switches. Forecast penetration: from near-zero today to 35% of all optical networking by 2030. Every one of those lasers is grown using a process called molecular beam epitaxy; MBE. A process that deposits semiconductor materials one atomic layer at a time, under ultra-high vacuum, with tolerances measured in atoms. The problem: the entire industry’s MBE infrastructure was built for 150mm and 200mm wafers. Silicon photonics runs on 300mm production lines, the same wafer size used in leading-edge logic fabs. There was no MBE system compatible with 300mm production lines. Until $ALRIB built one. Meet ROSIE; Riber Oxide on Silicon Epitaxy is the first MBE platform engineered specifically for 300mm silicon photonics production lines. No other equipment company makes this. The first two systems were ordered in 2025. ROSIE 2 the dual-chamber production version, goes into manufacturing in 2026. This is Year 0 of the ramp. Analyst consensus price target: €6. Current price: €15+. The gap exists because analysts are modeling Riber as a €40M scientific instruments company. Not one single sell-side model contains ROSIE as a separate revenue line. Silicon photonics is a $17B market by 2035. Riber’s current revenue: €40M. Market cap: €320M (~$340M USD). If ROSIE becomes the production standard for 300mm silicon photonics the way MOCVD became the standard for LED manufacturing, the revenue trajectory and the multiple both re-rate from here. Two constraints. Two chokepoints. One sits between every HBM die and every AI GPU that ships. The other is the only equipment that can grow the lasers replacing copper in AI infrastructure. Both are being priced on the wrong metrics. The market finds them eventually. This is not financial advice. Do your own due diligence. For full disclosure I haven’t taken a position myself, yet. They are both on my watchlist. I'm considering adding one of them to mmy short-term portfolio. $ALRIB looks like the most asymmetrical setup. A potential ten-bagger. $COHU the more safe-play. 3-5X. A potential $OUST look a like setup. @ParadisLabs any thoughts? I can’t call out @aleabitoreddit since I’m blocked, apparently. I'm also curious on other great investors perspective here: @moninvestor @Kaizen_Investor and @daniel_koss -BP

